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SPSS Tables™ 14.0

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Preface

SPSS 14.0 is a comprehensive system for analyzing data. The SPSS Tables optionaladd-on module provides the additional analytic techniques described in this manual.The Tables add-on module must be used with the SPSS 14.0 Base system and iscompletely integrated into that system.

Installation

To install the SPSS Tables add-on module, run the License Authorization Wizard usingthe authorization code that you received from SPSS Inc. For more information, see theinstallation instructions supplied with the SPSS Tables add-on module.

Compatibility

SPSS is designed to run on many computer systems. See the installation instructionsthat came with your system for specific information on minimum and recommendedrequirements.

Serial Numbers

Your serial number is your identification number with SPSS Inc. You will need thisserial number when you contact SPSS Inc. for information regarding support, payment,or an upgraded system. The serial number was provided with your Base system.

Customer Service

If you have any questions concerning your shipment or account, contact your localoffice, listed on the SPSS Web site at http://www.spss.com/worldwide. Please haveyour serial number ready for identification.

iii

Training Seminars

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The services of SPSS Technical Support are available to maintenance customers.Customers may contact Technical Support for assistance in using SPSS or forinstallation help for one of the supported hardware environments. To reach TechnicalSupport, see the SPSS Web site at http://www.spss.com, or contact your local office,listed on the SPSS Web site at http://www.spss.com/worldwide. Be prepared to identifyyourself, your organization, and the serial number of your system.

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The SPSS Statistical Procedures Companion, by Marija Norušis, has been publishedby Prentice Hall. A new version of this book, updated for SPSS 14.0, is planned.The SPSS Advanced Statistical Procedures Companion, also based on SPSS 14.0, isforthcoming. The SPSS Guide to Data Analysis for SPSS 14.0 is also in development.Announcements of publications available exclusively through Prentice Hall will beavailable on the SPSS Web site at http://www.spss.com/estore (select your homecountry, and then click Books).

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iv

About This Manual

This manual documents the graphical user interface for the procedures included in theSPSS Tables add-on module. Illustrations of dialog boxes are taken from SPSS forWindows. Dialog boxes in other operating systems are similar. Detailed informationabout the command syntax for features in the SPSS Tables add-on module is availablein two forms: integrated into the overall Help system and as a separate document inPDF form in the SPSS 14.0 Command Syntax Reference, available from the Help menu.

Contacting SPSS

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Contents

1 Getting Started with SPSS Tables 1

Table Structure and Terminology. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Pivot Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1Variables and Level of Measurement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2Rows, Columns, and Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Stacking . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3Crosstabulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Nesting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

Tables for Variables with Shared Categories . . . . . . . . . . . . . . . . . . . . . . . . . . 6Multiple Response Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6Totals and Subtotals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

Custom Summary Statistics for Totals . . . . . . . . . . . . . . . . . . . . . . . . . . . 8Sample Data File. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9Building a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

Opening the Custom Table Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11Selecting Row and Column Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . 12Inserting Totals and Subtotals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17Summarizing Scale Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

2 Table Builder Interface 29

Building Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29To Build a Table . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Stacking Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33Nesting Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

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Showing and Hiding Variable Names and/or Labels . . . . . . . . . . . . . . . . 37Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37Categories and Totals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46Tables of Variables with Shared Categories (Comperimeter Tables) . . . . 51Customizing the Table Builder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

Custom Tables: Options Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52Custom Tables: Titles Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54Custom Tables: Test Statistics Tab . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3 Simple Tables for Categorical Variables 59

A Single Categorical Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60Percentages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62Totals. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64

Crosstabulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66Percentages in Crosstabulations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67Controlling Display Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68Marginal Totals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69

Sorting and Excluding Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70

4 Stacking, Nesting, and Layers with CategoricalVariables 77

Stacking Categorical Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77Stacking with Crosstabulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79

Nesting Categorical Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 80Suppressing Variable Labels . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83Nested Crosstabulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85

Layers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88Two Stacked Categorical Layer Variables . . . . . . . . . . . . . . . . . . . . . . . 91

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Two Nested Categorical Layer Variables . . . . . . . . . . . . . . . . . . . . . . . . 93

5 Totals and Subtotals for Categorical Variables 95

Simple Total for a Single Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95What You See Is What Gets Totaled . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97Display Position of Totals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Totals for Nested Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98Layer Variable Totals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101Subtotals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103

What You See Is What Gets Subtotaled . . . . . . . . . . . . . . . . . . . . . . . . 105Hiding Subtotaled Categories. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106Layer Variable Subtotals . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110

6 Tables for Variables with Shared Categories 111

Table of Counts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112Table of Percentages . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115Totals and Category Control . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117Nesting in Tables with Shared Categories . . . . . . . . . . . . . . . . . . . . . . . . . . 120

7 Summary Statistics 123

Summary Statistics Source Variable . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124Summary Statistics Source for Categorical Variables. . . . . . . . . . . . . . 124Summary Statistics Source for Scale Variables . . . . . . . . . . . . . . . . . . 127

Stacked Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 130

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Custom Total Summary Statistics for Categorical Variables . . . . . . . . . . . . . 133Displaying Category Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

8 Summarizing Scale Variables 141

Stacked Scale Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141Multiple Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143Count, Valid N, and Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144Different Summaries for Different Variables . . . . . . . . . . . . . . . . . . . . . . . . 146Group Summaries in Categories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 148

Multiple Grouping Variables. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149Nesting Categorical Variables within Scale Variables . . . . . . . . . . . . . 152

9 Test Statistics 155

Tests of Independence (Chi-Square) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155Effects of Nesting and Stacking on Tests of Independence. . . . . . . . . . 159

Comparing Column Means . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161Effects of Nesting and Stacking on Column Means Tests . . . . . . . . . . . 164

Comparing Column Proportions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167Effects of Nesting and Stacking on Column Proportions Tests . . . . . . . 171

A Note on Weights and Multiple Response Sets . . . . . . . . . . . . . . . . . . . . . 174

10 Multiple Response Sets 175

Counts, Responses, Percentages, and Totals . . . . . . . . . . . . . . . . . . . . . . . 175Using Multiple Response Sets with Other Variables . . . . . . . . . . . . . . . . . . 179

Statistics Source Variable and Available Summary Statistics . . . . . . . . 181

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Multiple Category Sets and Duplicate Responses . . . . . . . . . . . . . . . . . . . . 182Significance Testing with Multiple Response Sets. . . . . . . . . . . . . . . . . . . . 185

Tests of Independence with Multiple Response Sets . . . . . . . . . . . . . . 185Comparing Column Means with Multiple Response Sets . . . . . . . . . . . 188

11 Missing Values 191

Tables without Missing Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 192Including Missing Values in Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 194

12 Formatting and Customizing Tables 197

Summary Statistics Display Format . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 198Display Labels for Summary Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . . 202Column Width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 204Display Value for Empty Cells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 206

Display Value for Missing Statistics . . . . . . . . . . . . . . . . . . . . . . . . . . . 206Changing the Default TableLook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 209

13 TABLES Command Syntax Converter 211

Index 215

xi

Chapter

1Getting Started with SPSS Tables

Many procedures in SPSS produce results in the form of tables. The SPSS Tablesadd-on module, however, offers special features designed to support a wide variety ofcustomized reporting capabilities. Many of the custom features are particularly usefulfor survey analysis and marketing research.

This guide assumes that you already know the basics of using SPSS. If you areunfamiliar with the basic operation of SPSS, see the introductory tutorial provided withthe software. From the menu bar in any open SPSS window, choose:Help

Tutorial

Table Structure and Terminology

SPSS Tables can produce a wide variety of customized tables. While you can discovera great deal of its capabilities simply by experimenting with the table builder interface,it may be helpful to know something about basic table structure in SPSS and the termswe use to describe different structural elements that you can use in a table.

Pivot Tables

Tables produced by SPSS Tables are displayed as pivot tables in the Viewer window.Pivot tables provide a great deal of flexibility over the formatting and presentationof tables.

For detailed information about working with pivot tables, use the Help system.

E From the menus in any open SPSS window, choose:Help

Topics

1

2

Chapter 1

E In the Contents pane, double-click Base System.

E Then double-click Pivot Tables in the expanded contents list.

Variables and Level of Measurement

To a certain extent, what you can do with a variable in a table is limited by its definedlevel of measurement. The Custom Tables procedure makes a distinction between twobasic types of variables, based on level of measurement:

Categorical. Data with a limited number of distinct values or categories (for example,gender or religion). Also referred to as qualitative data. Categorical variables can bestring (alphanumeric) data or numeric variables that use numeric codes to representcategories (for example, 0 = Female and 1 = Male). Categorical variables can befurther divided into:

Nominal. A variable can be treated as nominal when its values represent categorieswith no intrinsic ranking; for example, the department of the company in whichan employee works. Examples of nominal variables include region, zip code,or religious affiliation.

Ordinal. A variable can be treated as ordinal when its values represent categorieswith some intrinsic ranking; for example, levels of service satisfaction fromhighly dissatisfied to highly satisfied. Examples of ordinal variables includeattitude scores representing degree of satisfaction or confidence and preferencerating scores.

Variables defined as nominal or ordinal in the Data Editor are treated as categoricalvariables in the Custom Tables procedure.

Scale. A variable can be treated as scale when its values represent ordered categorieswith a meaningful metric, so that distance comparisons between values are appropriate.Examples of scale variables include age in years and income in thousands of dollars.Also referred to as quantitative, or continuous, data. Variables defined as scale in theData Editor are treated as scale variables in the Custom Tables procedure.

3

Getting Started with SPSS Tables

Value Labels

For categorical variables, the preview displayed on the canvas pane in the tablebuilder relies on defined value labels. The categories displayed in the table are, infact, the defined value labels for that variable. If there are no defined value labelsfor the variable, the preview displays two generic categories. The actual number ofcategories that will be displayed in the final table is determined by the number ofdistinct values that occur in the data. The preview simply assumes that there will beat least two categories.

Additionally, some custom table-building features are not available for categoricalvariables that have no defined value labels.

Rows, Columns, and Cells

Each dimension of a table is defined by a single variable or a combination of variables.Variables that appear down the left side of a table are called row variables. Theydefine the rows in a table. Variables that appear across the top of a table are calledcolumn variables. They define the columns in a table. The body of a table is madeup of cells, which contain the basic information conveyed by the table—counts,sums, means, percentages, and so on. A cell is formed by the intersection of a rowand column of a table.

Stacking

Stacking can be thought of as taking separate tables and pasting them together intothe same display. For example, you could display information on Gender and Agecategory in separate sections of the same table.

Figure 1-1Stacked variables

4

Chapter 1

Although the term “stacking” typically denotes a vertical display, you can also stackvariables horizontally.

Figure 1-2Horizontal stacking

Crosstabulation

Crosstabulation is a basic technique for examining the relationship between twocategorical variables. For example, using Age category as a row variable and Genderas a column variable, you can create a two-dimensional crosstabulation that shows thenumber of males and females in each age category.

Figure 1-3Simple two-dimensional crosstabulation

Nesting

Nesting, like crosstabulation, can show the relationship between two categoricalvariables, except one variable is nested within the other in the same dimension. Forexample, you could nest Gender within Age category in the row dimension, showingthe number of males and females in each age category.

5


In this example, the nested table displays essentially the same information as acrosstabulation of the same two variables.

Figure 1-4Nested variables

Layers

You can use layers to add a dimension of depth to your tables, creatingthree-dimensional “cubes.” Layers are, in fact, quite similar to nesting; the primarydifference is that only one layer category is visible at a time. For example, using Agecategory as the row variable and Gender as a layer variable produces a table in whichinformation for males and females is displayed in different layers of the table.

Figure 1-5Layered variables

6

Chapter 1

Tables for Variables with Shared Categories

Surveys often contain many questions with a common set of possible responses. Forexample, our sample survey contains a number of variables concerning confidence invarious public and private institutions and services, all with the same set of responsecategories: 1 = A great deal, 2 = Only some, and 3 = Hardly any. You can use stackingto display these related variables in the same table—and you can display the sharedresponse categories in the columns of the table.

Figure 1-6Stacked variables with shared response categories in columns

Multiple Response Sets

Multiple response sets use multiple variables to record responses to questions forwhich the respondent can give more than one answer. For example, our samplesurvey asks the question, “Which of the following sources do you rely on for news?”Respondents can select any combination of five possible choices: Internet, television,radio, newspapers, and news magazines. Each of these choices is stored as a separatevariable in the data file, and together they make a multiple response set. With SPSSTables, you can define a multiple response set based on these variables and use thatmultiple response set in the tables you create.

7


Figure 1-7Multiple response set displayed in a table

You may notice in this example that the percentages total to more than 100%. Becauseeach respondent may choose more than one answer, the total number of responses canbe greater than the total number of respondents.

Totals and Subtotals

SPSS Tables provides a great deal of control over the display of totals and subtotals,including:

Overall row and column totals

Group totals for nested, stacked, and layered tables

Subgroup totals

8

Chapter 1

Figure 1-8Subtotals, group totals, and table totals

Custom Summary Statistics for Totals

For tables that contain totals or subtotals, you can have different summary statisticsthan the summaries displayed for each category. For example, you could display countsfor an ordinal categorical row variable and display the mean for the “total” statistic.

Figure 1-9Categorical variable and summary statistics in the same dimension

9


Sample Data File

Most of the examples presented here use the data file survey_sample.sav. This datafile is a fictitious survey of several thousand people, containing basic demographicinformation and responses to a variety of questions, ranging from political views totelevision viewing habits.

All sample files used in these examples are located in the Tutorial\sample_filesfolder within the folder in which SPSS is installed.

Building a Table

Before you can build a table, you need some data to use in the table.

E From the menus, choose:File

OpenData...

Figure 1-10File menu, Open

10

Chapter 1

Alternatively, you can use the Open File button on the toolbar.

Figure 1-11Open File toolbar button

This opens the Open File dialog box.

Figure 1-12Sample_files folder displayed in Open File dialog box

E To use the data file in this example, use the Open File dialog box to navigate to theTutorial\sample_files folder, located in the folder in which SPSS is installed (typically,C:\Program Files\SPSS).

E Select survey_sample.sav and then click Open.

11


Opening the Custom Table Builder

E To open the custom table builder, from the menus, choose:Analyze

TablesCustom Tables...

Figure 1-13Analyze menu, Tables

12

Chapter 1

This opens the custom table builder.

Figure 1-14Custom table builder

Selecting Row and Column Variables

To create a table, you simply drag and drop variables where you want them to appearin the table.

E Select (click) Age category in the variable list and drag and drop it into the Rowsarea on the canvas pane.

13


Figure 1-15Selecting a row variable

The canvas pane displays the table that would be created using this single row variable.The preview does not display the actual values that would be displayed in the table;

it displays only the basic layout of the table.

14

Chapter 1

E Select Gender in the variable list and drag and drop it into the Columns area on thecanvas pane (you may have to scroll down the variable list to find this variable).

Figure 1-16Selecting a column variable

The canvas pane now displays a two-way crosstabulation of Age category by Gender.By default, counts are displayed in the cells for categorical variables. You can also

display row, column, and/or total percentages.

15


E Right-click on Age category on the canvas pane and select Summary Statistics fromthe pop-up context menu.

Figure 1-17Context menu for categorical variables on canvas pane

E In the Summary Statistics dialog box, select Row N % in the Statistics list and click thearrow button to add it to the Display list.

16

Chapter 1

Now both the counts and row percentages will be displayed in the table.

Figure 1-18Summary Statistics dialog box for categorical variables

E Click Apply to Selection to save these settings and return to the table builder.

17


The canvas pane reflects the changes you have made, displaying columns for bothcounts and row percentages.

Figure 1-19Counts and row percentages displayed on canvas pane

Inserting Totals and Subtotals

Totals are not displayed by default in custom tables, but it is easy to add both totalsand subtotals to a table.

E Right-click on Age category on the canvas pane and select Categories and Totals fromthe pop-up context menu.

E In the Categories and Totals dialog box, select (click) 3.00 in the Value(s) list.

E In the Label text field in the New Subtotal group, type Subtotal < 45.

E Then click the Insert button.

This inserts a row containing the subtotal for the first three age categories.

E Select (click) 6.00 in the Value(s) list.

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E In the Label text field in the New Subtotal group, type Subtotal 45 +.

E Then click the Insert button.

This inserts a row containing the subtotal for the last three age categories.

E To include an overall total, select the Total check box in the Show group.

Figure 1-20Inserting totals and subtotals

E Then click Apply.

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The canvas pane preview now includes rows for the two subtotals and the overall total.

Figure 1-21Total and subtotals on canvas pane

E Click OK to produce this table.

The table is displayed in the SPSS Viewer.

Figure 1-22Crosstabulation with totals and subtotals

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Summarizing Scale Variables

A simple crosstabulation of two categorical variables displays counts or percentagesin the cells of the table, but you can also display summaries of scale variables in thecells of the table.

E To open the custom table builder again, from the menus, choose:Analyze


E Click Reset to clear any previous selections.

E Select (click) Age category in the variable list and drag and drop it into the Rowsarea on the canvas pane.

Figure 1-23Selecting a row variable

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E Select Hours per day watching TV in the variable list and drag and drop it to the rightof Age category in the row dimension of the table.

Figure 1-24Dragging and dropping a scale variable into the row dimension

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Now, instead of category counts, the table will display the mean (average) number ofhours of television watched for each age category.

Figure 1-25Scale variable summarized in table cells

The mean is the default summary statistic for scale variables. You can add or changethe summary statistics displayed in the table.

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E Right-click the scale variable on the canvas pane, and select Summary Statistics fromthe pop-up context menu.

Figure 1-26Context menu for scale variables in table preview

E In the Summary Statistics dialog box, select Median in the Statistics list and click thearrow button to add it to the Display list.

Now both the mean and the median will be displayed in the table.

Figure 1-27Summary Statistics dialog box for scale variables

E Click Apply to Selection to save these settings and return to the table builder.

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Chapter 1

The canvas pane now shows that both the mean and median will be displayed in thetable.

Figure 1-28Mean and median scale summaries displayed on canvas pane

Before creating this table, let’s clean it up a bit.

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E Right-click on Hours per day... on the canvas pane and deselect (uncheck) Show

Variable Label on the pop-up context menu.

Figure 1-29Suppressing the display of variable labels

The column is still displayed in the table preview (with the variable label text grayedout), but this column will not be displayed in the final table.

E Click the Titles tab in the table builder.

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Chapter 1

E Enter a descriptive title for the table, such as Average Daily Number of Hours ofTelevision Watched by Age Category.

Figure 1-30Custom Tables dialog box, Titles tab

E Click OK to create the table.

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The table is displayed in the SPSS Viewer window.

Figure 1-31Mean and median number of TV hours by age category

Chapter

2Table Builder Interface

Custom Tables uses a simple drag-and-drop table builder interface that allows you topreview your table as you select variables and options. It also provides a level offlexibility not found in a typical dialog box, including the ability to change the size ofthe window and the size of the panes within the window.

Building TablesFigure 2-1Custom Tables dialog box, Table tab

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Chapter 2

You select the variables and summary measures that will appear in your tables onthe Table tab in the table builder.

Variable list. The variables in the data file are displayed in the top left pane of thewindow. Custom Tables distinguishes between two different measurement levels forvariables and handles them differently depending on the measurement level:

Categorical. Data with a limited number of distinct values or categories (for example,gender or religion). Categorical variables can be string (alphanumeric) or numericvariables that use numeric codes to represent categories (for example, 0 = male and1 = female). Also referred to as qualitative data. Categorical variables can be eithernominal or ordinal:



Scale. Data measured on an interval or ratio scale, where the data values indicate boththe order of values and the distance between values. For example, a salary of $72,195is higher than a salary of $52,398, and the distance between the two values is $19,797.Also referred to as quantitative or continuous data.

Categorical variables define categories (row, columns, and layers) in the table, and thedefault summary statistic is the count (number of cases in each category). For example,a default table of a categorical gender variable would simply display the number ofmales and the number of females.

Scale variables are typically summarized within categories of categorical variables,and the default summary statistic is the mean. For example, a default table of incomewithin gender categories would display the mean income for males and the meanincome for females.

You can also summarize scale variables by themselves, without using a categoricalvariable to define groups. This is primarily useful for stacking summaries of multiplescale variables. For more information, see “Stacking Variables” on p. 33.

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Table Builder Interface


Custom Tables also supports a special kind of “variable” called a multiple responseset. Multiple response sets are not really variables in the normal sense. You cannotsee them in the Data Editor, and other procedures do not recognize them. Multipleresponse sets use multiple variables to record responses to questions where therespondent can give more than one answer. Multiple response sets are treated likecategorical variables, and most of the things you can do with categorical variables, youcan also do with multiple response sets. For more information, see “Multiple ResponseSets” in Chapter 10 on p. 175.

An icon next to each variable in the variable list identifies the variable type.

Data TypeMeasurementLevel Numeric String Date Time

Scale n/a

Ordinal

Nominal

Multiple response set, multiple categories

Multiple response set, multiple dichotomies

You can change the measurement level of a variable in the table builder byright-clicking the variable in the variable list and selecting Categorical or Scale fromthe pop-up context menu. You can permanently change a variable’s measurement levelin the Variable View of the Data Editor. Variables defined as nominal or ordinal aretreated as categorical by Custom Tables.

Categories. When you select a categorical variable in the variable list, the definedcategories for the variable are displayed in the Categories list. These categories willalso be displayed on the canvas pane when you use the variable in a table. If the

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variable has no defined categories, the Categories list and the canvas pane will displaytwo placeholder categories: Category 1 and Category 2.

The defined categories displayed in the table builder are based on value labels,descriptive labels assigned to different data values (for example, numeric values of 0and 1, with value labels of male and female). You can define value labels in VariableView of the Data Editor or with Define Variable Properties on the Data menu in theData Editor window.

Canvas pane. You build a table by dragging and dropping variables onto the rows andcolumns of the canvas pane. The canvas pane displays a preview of the table that willbe created. The canvas pane does not show actual data values in the cells, but it shouldprovide a fairly accurate view of the layout of the final table. For categorical variables,the actual table may contain more categories than the preview if the data file containsunique values for which no value labels have been defined.

Normal view displays all of the rows and columns that will be included in thetable, including rows and/or columns for summary statistics and categories ofcategorical variables.

Compact view shows only the variables that will be in the table, without a previewof the rows and columns that the table will contain.

Basic Rules and Limitations for Building a Table

For categorical variables, summary statistics are based on the innermost variable inthe statistics source dimension.

The default statistics source dimension (row or column) for categorical variablesis based on the order in which you drag and drop variables into the canvas pane.For example, if you drag a variable to the rows tray first, the row dimension isthe default statistics source dimension.

Scale variables can be summarized only within categories of the innermost variablein either the row or column dimension. (You can position the scale variable at anylevel of the table, but it is summarized at the innermost level.)

Scale variables cannot be summarized within other scale variables. You canstack summaries of multiple scale variables or summarize scale variables withincategories of categorical variables. You cannot nest one scale variable withinanother or put one scale variable in the row dimension and another scale variablein the column dimension.

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To Build a Table

E From the menus, choose:Analyze


E Drag and drop one or more variables to the row and/or column areas of the canvas pane.


To delete a variable from the canvas pane in the table builder:

E Select (click) the variable on the canvas pane.

E Drag the variable anywhere outside the canvas pane, or press the Delete key.

To change the measurement level of a variable:

E Right-click the variable in the variable list (you can do this only in the variable list,not on the canvas).

E Select Categorical or Scale from the pop-up context menu.

Stacking Variables


To Stack Variables

E In the variable list, select all of the variables you want to stack, then drag and dropthem together into the rows or columns of the canvas pane.

or

E Drag and drop variables separately, dropping each variable either above or belowexisting variables in the rows or to the right or left of existing variables in the columns.

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Chapter 2

Figure 2-2Stacked variables

For more information, see “Stacking Categorical Variables” in Chapter 4 on p. 77.

Nesting Variables

Nesting, like crosstabulation, can show the relationship between two categoricalvariables, except that one variable is nested within the other in the same dimension. Forexample, you could nest Gender within Age category in the row dimension, showingthe number of males and females in each age category.

You can also nest a scale variable within a categorical variable. For example,you could nest Income within Gender, showing separate mean (or median or othersummary measure) income values for males and females.

To Nest Variables

E Drag and drop a categorical variable into the row or column area of the canvas pane.

E Drag and drop a categorical or scale variable to the left or right of the categorical rowvariable or above or below the categorical column variable.

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Figure 2-3Nested categorical variables

Figure 2-4Scale variable nested within a categorical variable

Note: Technically, the preceding table is an example of a categorical variable nestedwithin a scale variable, but the resulting information conveyed in the table is essentiallythe same as nesting the scale variable within the categorical variable, without redundantlabels for the scale variable. (Try it the other way around, and you will understand.)

For more information, see “Nesting Categorical Variables” in Chapter 4 on p. 80.

Layers

You can use layers to add a dimension of depth to your tables, creatingthree-dimensional “cubes.” Layers are similar to nesting or stacking; the primarydifference is that only one layer category is visible at a time. For example, using Agecategory as the row variable and Gender as a layer variable produces a table in whichinformation for males and females is displayed in different layers of the table.

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To Create Layers

E Click Layers on the Table tab in the table builder to display the Layers list.

E Drag and drop the scale or categorical variable(s) that will define the layers into theLayers list. You can also drag and drop variables onto the Layers button withoutdisplaying the Layers list.

Figure 2-5Layered variables

You cannot mix scale and categorical variables in the Layers list. All variables must beof the same type. Multiple response sets are treated as categorical for the Layers list.Scale variables in the layers are always stacked.

If you have multiple categorical layer variables, layers can be stacked or nested.

Show each category as a layer is equivalent to stacking. A separate layer will bedisplayed for each category of each layer variable. The total number of layers issimply the sum of the number of categories for each layer variable. For example,if you have three layer variables, each with three categories, the table will havenine layers.

Show each combination of categories as a layer is equivalent to nesting orcrosstabulating layers. The total number of layers is the product of the number ofcategories for each layer variable. For example, if you have three variables, eachwith three categories, the table will have 27 layers.

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Showing and Hiding Variable Names and/or Labels

The following options are available for the display of variable names and labels:

Show only variable labels. For any variables without defined variable labels, thevariable name is displayed. This is the default setting.

Show only variable names.

Show both variable labels and variable names.

Don’t show variable names or variable labels. Although the column/row thatcontains the variable label or name will still be displayed in the table preview onthe canvas pane, this column/row will not be displayed in the actual table.

To show or hide variable labels or variable names:

E Right-click the variable in the table preview on the canvas pane.

E Select Show Variable Label or Show Variable Name from the pop-up context menu totoggle the display of labels or names on or off. A check mark next to the selectionindicates that it will be displayed.

Summary Statistics

The Summary Statistics Categorical Variables dialog box allows you to:

Add and remove summary statistics from a table.

Change the labels for the statistics.

Change the order of the statistics.

Change the format of the statistics, including the number of decimal positions.

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Figure 2-6Summary Statistics Categorical Variables dialog box

The summary statistics (and other options) available here depend on the measurementlevel of the summary statistics source variable. The source of summary statistics (thevariable on which the summary statistics are based) is determined by:

Measurement level. If a table (or a table section in a stacked table) contains a scalevariable, summary statistics are based on the scale variable.

Variable selection order. The default statistics source dimension (row or column)for categorical variables is based on the order in which you drag and drop variablesonto the canvas pane. For example, if you drag a variable to the rows area first, therow dimension is the default statistics source dimension.

Nesting. For categorical variables, summary statistics are based on the innermostvariable in the statistics source dimension.

A stacked table may have multiple summary statistics source variables (both scale andcategorical), but each table section has only one summary statistics source.

To Change the Summary Statistics Source Dimension

E Select the dimension (rows, columns, or layers) from the Source drop-down list in theSummary Statistics group of the Table tab.

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To Control the Summary Statistics Displayed in a Table

E Select (click) the summary statistics source variable on the canvas pane of the Table tab.

E In the Define group of the Table tab, click Summary Statistics.

or

E Right-click the summary statistics source variable on the canvas pane and selectSummary Statistics from the pop-up context menu.

E Select the summary statistics you want to include in the table. You can use the arrow tomove selected statistics from the Statistics list to the Display list, or you can drag anddrop selected statistics from the Statistics list into the Display list.

E Click the up or down arrows to change the display position of the currently selectedsummary statistic.

E Select a display format from the Format drop-down list for the selected summarystatistic.

E Enter the number of decimals to display in the Decimals cell for the selected summarystatistic.

E Click Apply to Selection to include the selected summary statistics for the currentlyselected variables on the canvas pane.

E Click Apply to All to include the selected summary statistics for all stacked variables ofthe same type on the canvas pane.

Note: Apply to All differs from Apply to Selection only for stacked variables of thesame type already on the canvas pane. In both cases, the selected summary statisticsare automatically included for any additional stacked variables of the same type thatyou add to the table.

Summary Statistics for Categorical Variables

The basic statistics available for categorical variables are counts and percentages. Youcan also specify custom summary statistics for totals and subtotals. These customsummary statistics include measures of central tendency (such as mean and median)and dispersion (such as standard deviation) that may be suitable for some ordinal

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categorical variables. For more information, see “Custom Total Summary Statistics forCategorical Variables” on p. 43.

Count. Number of cases in each cell of the table or number of responses for multipleresponse sets.

Unweighted Count. Unweighted number of cases in each cell of the table.

Column percentages. Percentages within each column. The percentages in each columnof a subtable (for simple percentages) sum to 100%. Column percentages are typicallyuseful only if you have a categorical row variable.

Row percentages. Percentages within each row. The percentages in each row of asubtable (for simple percentages) sum to 100%. Row percentages are typically usefulonly if you have a categorical column variable.

Layer Row and Layer Column percentages. Row or column percentages (for simplepercentages) sum to 100% across all subtables in a nested table. If the table containslayers, row or column percentages sum to 100% across all nested subtables in eachlayer.

Layer percentages. Percentages within each layer. For simple percentages, cellpercentages within the currently visible layer sum to 100%. If you do not have anylayer variables, this is equivalent to table percentages.

Table percentages. Percentages for each cell are based on the entire table. All cellpercentages are based on the same total number of cases and sum to 100% (for simplepercentages) over the entire table.

Subtable percentages. Percentages in each cell are based on the subtable. All cellpercentages in the subtable are based the same total number of cases and sum to 100%within the subtable. In nested tables, the variable that precedes the innermost nestinglevel defines subtables. For example, in a table of Marital status within Gender withinAge category, Gender defines subtables.

Multiple response sets can have percentages based on cases, responses, or counts. Formore information, see “Summary Statistics for Multiple Response Sets” on p. 41.

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Stacked Tables

For percentage calculations, each table section defined by a stacking variable is treatedas a separate table. Layer Row, Layer Column, and Table percentages sum to 100%(for simple percentages) within each stacked table section. The percentage base fordifferent percentage calculations is based on the cases in each stacked table section.

Percentage Base

Percentages can be calculated in three different ways, determined by the treatment ofmissing values in the computational base:

Simple percentage. Percentages are based on the number of cases used in the table andalways sum to 100%. If a category is excluded from the table, cases in that categoryare excluded from the base. Cases with system-missing values are always excludedfrom the base. Cases with user-missing values are excluded if user-missing categoriesare excluded from the table (the default) or included if user-missing categories areincluded in the table. Any percentage that does not have Valid N or Total N in its nameis a simple percentage.

Total N percentage. Cases with system-missing and user-missing values are added tothe Simple percentage base. Percentages may sum to less than 100%.

Valid N percentage. Cases with user-missing values are removed from the Simplepercentage base even if user-missing categories are included in the table.

Note: Cases in manually excluded categories other than user-missing categories arealways excluded from the base.

Summary Statistics for Multiple Response Sets

The following additional summary statistics are available for multiple response sets.

Col/Row/Layer Responses %. Percentage based on responses.

Col/Row/Layer Responses % (Base: Count). Responses are the numerator and total countis the denominator.

Col/Row/Layer Count % (Base: Responses). Count is the numerator and total responsesare the denominator.

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Layer Col/Row Responses %. Percentage across subtables. Percentage based onresponses.

Layer Col/Row Responses % (Base: Count). Percentages across subtables. Responses arethe numerator and total count is the denominator.

Layer Col/RowResponses % (Base: Responses). Percentages across subtables. Count isthe numerator and total responses is the denominator.

Responses. Count of responses.

Subtable/Table Responses %. Percentage based on responses.

Subtable/Table Responses % (Base: Count). Responses are the numerator and total countis the denominator.

Subtable/Table Count % (Base: Responses). Count is the numerator and total responsesare the denominator.

Summary Statistics for Scale Variables and Categorical Custom Totals

In addition to the counts and percentages available for categorical variables, thefollowing summary statistics are available for scale variables and as custom totaland subtotal summaries for categorical variables. These summary statistics are notavailable for multiple response sets or string (alphanumeric) variables.

Mean. Arithmetic average; the sum divided by the number of cases.

Median. Value above and below which half of the cases fall; the 50th percentile.

Mode. Most frequent value. If there is a tie, the smallest value is shown.

Minimum. Smallest (lowest) value.

Maximum. Largest (highest) value.

Missing. Count of missing values (both user- and system-missing).

Percentile. You can include the 5th, 25th, 75th, 95th, and/or 99th percentiles.

Range. Difference between maximum and minimum values.

Standard error of the mean. A measure of how much the value of the mean may varyfrom sample to sample taken from the same distribution. It can be used to roughlycompare the observed mean to a hypothesized value (that is, you can conclude that the

43


two values are different if the ratio of the difference to the standard error is less than–2 or greater than +2).

Standard deviation. A measure of dispersion around the mean. In a normal distribution,68% of the cases fall within one standard deviation of the mean and 95% of the casesfall within two standard deviations. For example, if the mean age is 45, with a standarddeviation of 10, 95% of the cases would be between 25 and 65 in a normal distribution(the square root of the variance).

Sum. Sum of the values.

Sum percentage. Percentages based on sums. Available for rows and columns (withinsubtables), entire rows and columns (across subtables), layers, subtables, and entiretables.

Total N. Count of non-missing, user-missing, and system-missing values. Does notinclude cases in manually excluded categories other than user-missing categories.

Valid N. Count of non-missing values. Does not include cases in manually excludedcategories other than user-missing categories.

Variance. A measure of dispersion around the mean, equal to the sum of squareddeviations from the mean divided by one less than the number of cases. The varianceis measured in units that are the square of those of the variable itself (the square ofthe standard deviation).

Stacked Tables

Each table section defined by a stacking variable is treated as a separate table, andsummary statistics are calculated accordingly.

Custom Total Summary Statistics for Categorical Variables

For tables of categorical variables that contain totals or subtotals, you can havedifferent summary statistics than the summaries displayed for each category. Forexample, you could display counts and column percentages for an ordinal categoricalrow variable and display the median for the “total” statistic.

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To create a table for a categorical variable with a custom total summary statistic:



The table builder will open.

E Drag and drop a categorical variable into the Rows or Columns area of the canvas.

E Right-click on the variable on the canvas and select Categories and Totals from thepop-up context menu.

E Click (check) the Total check box, and then click Apply.

E Right-click the variable again on the canvas and select Summary Statistics from thepop-up context menu.

E Click (check) Custom Summary Statistics for Totals and Subtotals, and then select thecustom summary statistics you want.

By default, all summary statistics, including custom summaries, are displayed in theopposite dimension from the dimension containing the categorical variable. Forexample, if you have a categorical row variable, summary statistics define columnsin the table, as in:

Figure 2-7Default position of summary statistics

To display summary statistics in the same dimension as the categorical variable:

E On the Table tab in the table builder, in the Summary Statistics group, select thedimension from the Position drop-down list.

For example, if the categorical variable is displayed in the rows, select Rows fromthe drop-down list.

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Figure 2-8Categorical variable and summary statistics in the same dimension

Summary Statistics Display Formats

The following display format options are available:

nnnn. Simple numeric.

nnnn%. Percentage sign appended to end of value.

Auto. Defined variable display format, including number of decimals.

N=nnnn. Displays N= before the value. This can be useful for counts, valid N, and totalN in tables where the summary statistics labels are not displayed.

(nnnn). All values enclosed in parentheses.

(nnnn)(neg. value). Only negative values enclosed in parentheses.

(nnnn%). All values enclosed in parentheses and a percentage sign appended to endof values.

n,nnn.n. Comma format. Comma used as grouping separator and period used asdecimal indicator regardless of locale settings.

n.nnn,n. Dot format. Period used as grouping separator and comma used as decimalindicator regardless of locale settings.

$n,nnn.n. Dollar format. Dollar sign displayed in front of value; comma used asgrouping separator and period used as decimal indicator regardless of locale settings.

CCA, CCB, CCC, CCD, CCE. Custom currency formats. The current defined format foreach custom currency is displayed in the list. These formats are defined on theCurrency tab in the SPSS Options dialog box (Edit menu, Options).

General Rules and Limitations

With the exception of Auto, the number of decimals is determined by the Decimalscolumn setting.

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With the exception of the comma, dollar, and dot formats, the decimal indicatorused is the one defined for the current locale in your Windows Regional Optionscontrol panel.

Although comma/dollar and dot will display either a comma or period respectivelyas the grouping separator, there is no display format available at creation time todisplay a grouping separator based on the current locale settings (defined in theWindows Regional Options control panel).

Categories and Totals

The Categories and Totals dialog box allows you to:

Reorder and exclude categories.

Insert subtotals and totals.

Include or exclude empty categories.

Include or exclude categories defined as containing missing values.

Include or exclude categories that do not have defined value labels.

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Figure 2-9Categories and Totals dialog box

This dialog box is available only for categorical variables and multiple responsesets. It is not available for scale variables.

For multiple selected variables with different categories, you cannot insertsubtotals, exclude categories, or manually reorder categories. This occurs onlyif you select multiple variables in the canvas preview and access this dialog boxfor all selected variables simultaneously. You can still perform these actions foreach variable separately.

For variables with no defined value labels, you can only sort categories and inserttotals.

To Access the Categories and Totals Dialog Box

E Drag and drop a categorical variable or multiple response set onto the canvas pane.

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Chapter 2

E Right-click the variable on the canvas pane, and select Categories and Totals fromthe pop-up context menu.

or

E Select (click) the variable on the canvas pane, and then click Categories and Totals inthe Define group on the Table tab.

You can also select multiple categorical variables in the same dimension on the canvaspane:

E Ctrl-click each variable on the canvas pane.

or

E Click outside the table preview on the canvas pane, and then click and drag to selectthe area that includes the variables you want to select.

or

E Right-click any variable in a dimension and select Select All [dimension] Variables toselect all of the variables in that dimension.

To Reorder Categories

To manually reorder categories:

E Select (click) a category in the list.

E Click the up or down arrow to move the category up or down in the list.

or

E Click in the Value(s) column for the category, and drag and drop it in a differentposition.

To Exclude Categories

E Select (click) a category in the list.

49


E Click the arrow next to the Exclude list.

or

E Click in the Value(s) column for the category and drag and drop it anywhere outsidethe list.

If you exclude any categories, any categories without defined value labels will alsobe excluded.

To Sort Categories

You can sort categories by data value, value label, cell count, or summary statistic inascending or descending order.

E In the Sort Categories group, click the By drop-down list and select the sort criterionyou want to use: value, label, count, or summary statistic (such as mean, median, ormode). The available summary statistics for sorting depends on the summary statisticsyou have selected to display in the table.

E Click the Order drop-down list to select the sort order (ascending or descending).

Sorting categories is not available if you have excluded any categories.

Subtotals

E Select (click) the category in the list that is the last category in the range of categoriesthat you want to include in the subtotal.

E To show only a subtotal and suppress the display of the categories that define thesubtotal, select Hide subtotaled categories.

E Click Insert. You can also modify the subtotal label text.

Totals

E Click the Total check box. You can also modify the total label text.

If the selected variable is nested within another variable, totals will be inserted foreach subtable.

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Display Position for Totals and Subtotals

Totals and subtotals can be displayed above or below the categories included in eachtotal.

If Below is selected in the Totals and Subtotals Appear group, totals appear aboveeach subtable, and all categories above and including the selected category (butbelow any preceding subtotals) are included in each subtotal.

If Above is selected in the Totals and Subtotals Appear group, totals appear beloweach subtable, and all categories below and including the selected category (butabove any preceding subtotals) are included in each subtotal.

Important: You should select the display position for subtotals before defining anysubtotals. Changing the display position affects all subtotals (not just the currentlyselected subtotal), and it also changes the categories included in the subtotals.

Custom Total and Subtotal Summary Statistics

You can display statistics other than “totals” in the Totals and Subtotals areas of thetable using the Summary Statistics dialog box. For more information, see “SummaryStatistics for Categorical Variables” on p. 39.

Note: If you select multiple custom total statistics that are also in the body of the tableand you hide the statistics labels, then the totals are resorted into the same order as inthe body of the table—and since the labels aren’t displayed, you may not know whateach total statistic actually represents. In general, selecting multiple statistics andhiding the statistics labels is probably not a good idea.

Totals, Subtotals, and Excluded Categories

Cases from excluded categories are not included in the calculation of totals.

Missing Values, Empty Categories, and Values without Value Labels

Missing values. This controls the display of user-missing values, or values defined ascontaining missing values (for example, a code of 99 to represent “not applicable” forpregnancy in males). By default, user-missing values are excluded. Select (check) thisoption to include user-missing categories in tables. Although the variable may containmore than one missing value category, the table preview on the canvas will displayonly one generic missing value category. All defined user-missing categories will

51


be included in the table. System-missing values (empty cells for numeric variablesin the Data Editor) are always excluded.

Empty categories. Empty categories are categories with defined value labels but nocases in that category for a particular table or subtable. By default, empty categoriesare included in tables. Deselect (uncheck) this option to exclude missing categoriesfrom the table.

Other values found when data are scanned. By default, category values in the data filethat do not have defined value labels are automatically included in tables. Deselect(uncheck) this option to exclude values without defined value labels from the table.If you exclude any categories with defined value labels, categories without definedvalue labels are also excluded.

Tables of Variables with Shared Categories (Comperimeter Tables)

Surveys often contain many questions with a common set of possible responses. Youcan use stacking to display these related variables in the same table, and you candisplay the shared response categories in the columns of the table.

To Create a Table for Multiple Variables with Shared Categories

E Drag and drop the categorical variables from the variable list into the Rows area ofthe canvas. The variables should be stacked. For more information, see “StackingVariables” on p. 33.

E From the Category Position drop-down list, select Row labels in columns.

Figure 2-10Stacked variables with shared response categories in columns

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For more information, see “Tables for Variables with Shared Categories” in Chapter6 on p. 111.

Customizing the Table Builder

Unlike standard dialog boxes, you can change the size of the table builder in the sameway that you can change the size of any standard window:

E Click and drag the top, bottom, either side, or any corner of the table builder todecrease or increase its size.

On the Table tab, you can also change the size of the variable list, the Categorieslist, and the canvas pane.

E Click and drag the horizontal bar between the variable list and the Categories list tomake the lists longer or shorter. Moving it down makes the variable list longer and theCategories list shorter. Moving it up does the reverse.

E Click and drag the vertical bar between the variable list and Categories list from thecanvas pane to make the lists wider or narrower. The canvas automatically resizes tofit the remaining space.

Custom Tables: Options Tab

The Options tab allows you to:

Specify what is displayed in empty cells and cells for which statistics cannot becomputed.

Control how missing values are handled in the computation of scale variablestatistics.

Set minimum and/or maximum data column widths.

Control the treatment of duplicate responses in multiple category sets.

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Figure 2-11Custom Tables dialog box, Options tab

Data Cell Appearance. Controls what is displayed in empty cells and cells for whichstatistics cannot be computed.

Empty cells. For table cells that contain no cases (cell count of 0), you can selectone of three display options: zero, blank, or a text value that you specify. The textvalue can be up to 255 characters long.

Statistics that Cannot be Computed. Text displayed if a statistic cannot be computed(for example, the mean for a category with no cases). The text value can be up to255 characters long. The default value is a period (.).

Width for Data Columns. Controls minimum and maximum column width for datacolumns. This setting does not affect columns widths for row labels.

TableLook settings. Uses the data column width specification from the currentdefault TableLook. You can create your own custom default TableLook to usewhen new tables are created, and you can control both row label column anddata column widths with a TableLook.

Custom. Overrides the default TableLook settings for data column width. Specifythe minimum and maximum data column widths for the table and the measurementunit: points, inches, or centimeters.

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Missing Values for Scale Variables. For tables with two or more scale variables, controlsthe handling of missing data for scale variable statistics.

Maximize use of available data (variable-by-variable deletion). All cases with validvalues for each scale variable are included in summary statistics for that scalevariable.

Use consistent case base across scale variables (listwise deletion). Cases withmissing values for any scale variables in the table are excluded from the summarystatistics for all scale variables in the table.

Count duplicate responses for multiple category sets. A duplicate response is the sameresponse for two or more variables in the multiple category set. By default, duplicateresponses are not counted, but this may be a perfectly valid condition that you do wantto include in the count (such as a multiple category set representing the manufacturerof the last three cars purchased by a survey respondent).

Custom Tables: Titles Tab

The Titles tab controls the display of titles, captions, and corner labels.

Figure 2-12Custom Tables dialog box, Titles tab

Title. Text that is displayed above the table.

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Caption. Text that is displayed below the table and above any footnotes.

Corner. Text that is displayed in the upper left corner of the table. Corner text isdisplayed only if the table contains row variables and if the pivot table row dimensionlabel property is set to Nested. This is not the default TableLook setting.

For more information, see “Changing the Default TableLook” in Chapter 12 on p. 209.

You can include the following automatically generated values in the table title, caption,or corner label:

Date. Current year, month, and day displayed in a format based on your currentWindows Regional Options settings.

Time. Current hour, minute, and second displayed in a format based on your currentWindows Regional Options settings.

Table Expression. Variables used in the table and how they are used in the table. If avariable has a defined variable label, the label is displayed. In the generated table, thefollowing symbols indicate how variables are used in the table:

+ indicates stacked variables.

> indicates nesting.

BY indicates crosstabulation or layers.

Custom Tables: Test Statistics Tab

The Test Statistics tab allows you to request various significance tests for your customtables, including:

Chi-square tests of independence.

Tests of the equality of column means.

Tests of the equality of column proportions.

Significance tests for multiple response sets and subtotals. (For information onsignificance testing for multiple response sets, see “Significance Testing withMultiple Response Sets” on p. 185.)

These tests are not available for tables in which category labels are moved out of theirdefault table dimension.

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Figure 2-13Custom Tables dialog box, Test Statistics tab

Tests of independence (Chi-square). This option produces a chi-square test ofindependence for tables in which at least one category variable exists in both the rowsand columns. You can also specify the alpha level of the test, which should be a valuegreater than 0 and less than 1.

Compare column means (t-tests). This option produces pairwise tests of the equality ofcolumn means for tables in which at least one category variable exists in the columnsand at least one scale variable exists in the rows. You can select whether the p valuesof the tests are adjusted using the Bonferroni method. You can also specify the alphalevel of the test, which should be a value greater than 0 and less than 1. Finally, whilethe variance for the means test is always based on just the categories compared formultiple response tests, for ordinary categorical variables it can be estimated from justthe categories compared or all categories.

Compare column proportions (z-tests). This option produces pairwise tests of the equalityof column proportions for tables in which at least one category variable exists in boththe columns and rows. You can select whether the p values of the tests are adjusted

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using the Bonferroni method. You can also specify the alpha level of the test, whichshould be a value greater than 0 and less than 1.

Use subtotals in place of subtotaled categories. If selected, then each subtotal replaces itscategories for significance testing. Otherwise, only subtotals for which the subtotaledcategories are hidden replace their categories for testing.

Include multiple response variables. If selected, tests are performed using categories ofmultiple response sets. Otherwise multiple response sets are ignored when performingtests.

Chapter

3Simple Tables for CategoricalVariables

Most tables you want to create will probably include at least one categorical variable.A categorical variable is one with a limited number of distinct values or categories (forexample, gender or religion). Categorical variables can be either nominal or ordinal:



An icon next to each variable in the variable list identifies the variable type.

Data TypeMeasurementLevel Numeric String Date Time

Scale n/a

Ordinal

Nominal

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Custom Tables is optimized for use with categorical variables that have defined valuelabels. For more information, see “Building Tables” in Chapter 2 on p. 29.

Sample Data File

The examples in this chapter use the data file survey_sample.sav. This file is located inthe Tutorial\sample_files folder within the folder in which SPSS is installed.

All examples provided here display variable labels in dialog boxes, sorted inalphabetical order. Variable list display properties are set on the General tab in theOptions dialog box (Edit menu, Options).

A Single Categorical Variable

Although a table of a single categorical variable may be one of the simplest tables youcan create, it may often be all you want or need.



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Simple Tables for Categorical Variables

E In the table builder, drag and drop Age category from the variable list to the Rowsarea on the canvas pane.

A preview of the table is displayed on the canvas pane. The preview doesn’t displayactual data values; it displays only placeholders where data will be displayed.

Figure 3-1Single categorical variable in rows in table builder canvas preview


The table is displayed in the Viewer window.

Figure 3-2Single categorical variable in rows

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In this simple table, the column heading Count isn’t really necessary, and you cancreate the table without this column heading.

E Open the table builder again (Analyze menu, Tables, Custom Tables).

E Select (click) Hide for Position in the Summary Statistics group.


Figure 3-3Single categorical variable without summary statistics column label

Percentages

In addition to counts, you can also display percentages. For a simple table of a singlecategorical variable, if the variable is displayed in rows, you probably want to look atcolumn percentages. Conversely, for a variable displayed in columns, you probablywant to look at row percentages.


E Deselect (uncheck) Hide for Position in the Summary Statistics group. Since this tablewill have two columns, you want to display the column labels so you know whateach column represents.

E Right-click Age category on the canvas pane and select Summary Statistics from thepop-up context menu.

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Figure 3-4Right-click context menu on canvas pane

E In the Summary Statistics dialog box, select Column N % in the Statistics list and clickthe arrow to add it to the Display list.

E In the Label cell in the Display list, delete the default label and type Percent.

Figure 3-5Summary Statistics Categorical Variables dialog box

E Click Apply to Selection and then click OK in the table builder to create the table.

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Figure 3-6Counts and column percentages

Totals

Totals are not automatically included in custom tables, but it’s easy to add totals to atable.


E Right-click Age category on the canvas pane and select Categories and Totals fromthe pop-up context menu.

E Select (click) Total in the Categories and Totals dialog box.

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E Click Apply and then click OK in the table builder to create the table.

Figure 3-8Counts, column percentages, and totals

For more information, see “Totals and Subtotals for Categorical Variables” in Chapter5 on p. 95.

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Crosstabulation

Crosstabulation is a basic technique for examining the relationship between twocategorical variables. For example, using Age category as a row variable and Genderas a column variable, you can create a two-dimensional crosstabulation that shows thenumber of males and females in each age category.

E Open the table builder (Analyze menu, Tables, Custom Tables).

E Click Reset to delete any previous selections in the table builder.


E Drag and drop Gender from the variable list to the Columns area on the canvas pane.(You may have to scroll down through the variable list to find this variable.)

Figure 3-9Crosstabulation in table builder canvas preview


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Figure 3-10Crosstabulation of Age category and Gender

Percentages in Crosstabulations

In a two-dimensional crosstabulation, both row and column percentages may provideuseful information.


E Right-click Gender on the canvas pane.

You may notice that Summary Statistics is disabled in the pop-up context menu. Thisis because you can only select summary statistics for the innermost variable in thestatistics source dimension. The default statistics source dimension (row or column)for categorical variables is based on the order in which you drag and drop variablesonto the canvas pane. In this example, we dragged Age category to the rows dimensionfirst—and since there aren’t any other variables in the rows dimension, Age categoryis the statistics source variable. You can change the statistics source dimension, butin this example, you don’t need to do that. For more information, see “SummaryStatistics” in Chapter 2 on p. 37.



E Select Row N % in the Statistics list and click the arrow to add it to the Display list.

E Click Apply to Selection and then click OK in the table builder to create the table.

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Figure 3-11Crosstabulation with row and column percentages

Controlling Display Format

You can control the display format, including the number of decimals displayedin summary statistics. For example, by default percentages are displayed with onedecimal and a percent sign. But what if you want the cell values to show two decimalsand no percent sign?



E For the two selected percentage summary statistics (Column N % and Row N %), selectnnnn.n from the Format drop-down list and type 2 in the Decimals cell for both of them.

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Figure 3-12Summary Statistics dialog box


Figure 3-13Formatted cell display for row and column percentages

Marginal Totals

It’s fairly common in crosstabulations to display marginal totals—totals for each rowand column. Since these aren’t included in Custom Tables by default, you need toexplicitly add them to your tables.



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E Drag and drop Gender from the variable list to the Columns area on the canvas pane.(You may have to scroll down through the variable list to find this variable.)


E Select (click) Total in the Categories and Totals dialog box and then click Apply.

E Right-click Gender on the canvas pane and select Categories and Totals from thepop-up context menu.

E Select (click) Total in the Categories and Totals dialog box and then click Apply.

E Select (click) Hide for Position in the Summary Statistics group. (Since you’redisplaying only counts, you don’t need to identify the “statistic” displayed in the datacells of the table.)


Figure 3-14Crosstabulation with marginal totals

Sorting and Excluding CategoriesBy default, categories are displayed in the ascending order of the data values that thecategory value labels represent. For example, although value labels of Less than 25,25 to 34, 35 to 44, ..., etc., are displayed for age categories, the actual underlyingdata values are 1, 2, 3, ..., etc., and it is those underlying data values that control thedefault display order of the categories.

You can easily change the order of the categories and also exclude categories thatyou don’t want displayed in the table.

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Sorting Categories

You can manually rearrange categories or sort categories in ascending or descendingorder of:

Data values.

Value labels.

Cell counts.

Summary statistics. The available summary statistics for sorting depends on thesummary statistics you have selected to display in the table.


E If Age category isn’t already displayed in the Rows area on the canvas pane, dragand drop it there.

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Both data values and the associated value labels are displayed in the current sort order,which in this case is still ascending order of data values.

Figure 3-15Default category order, ascending by data values

E In the Sort Categories group, select Descending from the Order drop-down list.

The sort order is now reversed.

E Select Labels from the By drop-down list.

The categories are now sorted in descending alphabetical order of the value labels.

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Figure 3-16Descending alphabetical sort order

Notice that the category labeled Less than 25 is at the top of the list. In alphabeticalsorting, letters come after numbers. Since this is the only label that starts with aletter and since the list is sorted in descending (reverse) order, this category sorts tothe top of the list.

If you want a particular category to appear at a different location in the list, youcan easily move it.

E Click the category labeled Less than 25 in the Label list.

E Click the down arrow to the right of the list. The category moves down one rowin the list.

E Keep clicking the down arrow until the category is at the bottom of the list.

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Figure 3-17Manually arranged categories

Excluding Categories

If there are some categories that you don’t want to appear in the table, you can excludethem.


E Click the arrow key to the left of the Exclude list.

E Click the category labeled 65 or older in the Label list.

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E Click the arrow key to the left of the Exclude list again.

The two categories are moved from the Display list to the Exclude list. If you changeyour mind, you can easily move them back to the Display list.

Figure 3-18Manually excluded categories


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Figure 3-19Table sorted by descending value label, some categories excluded

Notice that the totals are lower than they were before the two categories were excluded.This is because totals are based on the categories included in the table. Any excludedcategories are excluded from the total calculation. For more information, see “Totalsand Subtotals for Categorical Variables” in Chapter 5 on p. 95.

Chapter

4Stacking, Nesting, and Layerswith Categorical Variables

Stacking, nesting, and layers are all methods for displaying multiple variables in thesame table. This chapter focuses on using these techniques with categorical variables,although they can also be used with scale variables.

Sample Data File



Stacking Categorical Variables




E In the table builder, drag and drop Gender from the variable list to the Rows areaon the canvas pane.

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E Drag and drop Age category from the variable list to the Rows area below Gender.

The two variable are now stacked in the row dimension.

Figure 4-1Stacked categorical variables displayed on the canvas pane


Figure 4-2Table of categorical variables stacked in rows

You can also stack variables in columns in a similar fashion.

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Stacking, Nesting, and Layers with Categorical Variables

Stacking with Crosstabulation

A stacked table can include other variables in other dimensions. For example, youcould crosstabulate two variables stacked in the rows with a third variable displayed inthe column dimension.


E If Age category and Gender aren’t already stacked in the rows, follow the directionsabove for stacking them.

E Drag and drop Get news from internet from the variable list to the Columns area on thecanvas pane.


Figure 4-3Two stacked row variables crosstabulated with a column variable

Note: There are several variables with labels that start with Get news from ..., so it maybe difficult to distinguish between them in the variable list (since the labels may betoo wide to be displayed completely in the variable list). There are two ways to seethe entire variable label:

Position the mouse pointer on a variable in the list to display the entire label in apop-up ToolTip.

Click and drag the vertical bar that separates the variable and Categories lists fromthe canvas pane to make the lists wider.

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Figure 4-4Variable list widened to display complete variable labels

Nesting Categorical Variables

Nesting, like crosstabulation, can show the relationship between two categoricalvariables, except that one variable is nested within the other in the same dimension. Forexample, you could nest Gender within Age category in the row dimension, showingthe number of males and females in each age category.




E Drag and drop Gender from the variable list to the right of Age category in the Rowsarea.

The preview on the canvas pane now shows that the nested table will contain a singlecolumn of counts, with each cell containing the number of males or females in eachage category.

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Figure 4-5Gender nested within Age category

You may notice that the variable label Gender is displayed repeatedly, once for eachage category. You can minimize this kind of repetition by placing the variable with thefewest categories at the outermost level of the nesting.

E Click the variable label Gender on the canvas pane.

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E Drag and drop the variable as far to the left in the Rows area as you can.

Now instead of Gender being repeated six times, Age category is repeated twice. Thisis a less-cluttered table that will produce essentially the same results.

Figure 4-6Age category nested within Gender in table builder preview


Figure 4-7Table of Age category nested within Gender

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Suppressing Variable Labels

Another solution to redundant variable labels in nested tables is simply to suppressthe display of variable names or labels. Since the value labels for both Gender andAge category are probably sufficiently descriptive without the variable labels, we caneliminate the labels for both variables.


E Right-click Age category on the canvas pane and deselect (uncheck) Show Variable

Label on the pop-up context menu.

E Do the same for Gender.

Figure 4-8Suppressing variable labels via the context menu in the table builder

The variable labels are still displayed in the table preview, but they won’t be includedin the table.


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Figure 4-9Nested table without variable labels

If you want the variable labels included with the table somewhere—without displayingthem multiple times in the body of the table—you can include them in the table title orcorner label.


E Click the Titles tab.

E Click anywhere in the Title text box.

E Click Table Expression. The text &[Table Expression] is displayed in the Title textbox. This will generate a table title that includes the variable labels for the variablesused in the table.


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Figure 4-10Variable labels in table title

The greater than sign (>) in the title indicates that Age category is nested within Gender.

Nested Crosstabulation

A nested table can contain other variables in other dimensions. For example, you couldnest Age category within Gender in the rows and crosstabulate the nested rows with athird variable in the column dimension.


E If Age category isn’t already nested within Gender in the rows, follow the directionsabove for nesting them.

E Drag and drop Get news from internet from the variable list to the Columns area on thecanvas pane.

You may notice that the table is too large to display completely on the canvas pane. Youcan scroll up/down or right/left on the canvas pane to see more of the table preview, or:

Click Compact in the table builder to see a compact view. This displays onlythe variable labels, without any information on categories or summary statisticsincluded in the table.

Increase the size of the table builder by clicking and dragging any of the sidesor corners of the table builder.

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Figure 4-11Compact view on the canvas pane


Figure 4-12Nested crosstabulation

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Swapping Rows and Columns

What do you do if you spend a lot of time setting up a complex table and then decideit’s absolutely perfect—except that you want to switch the orientation, putting all ofthe row variables in the columns and vice versa? For example, you’ve created a nestedcrosstabulation with Age category and Gender nested in the rows, but now you wantthese two demographic variables nested in the columns instead.


E Right-click anywhere on the canvas pane and select Swap Row and Column Variables

from the pop-up context menu.

Figure 4-13Swapping row and column variables

The row and column variables have now been switched.

Before creating the table, let’s make a few modifications to make the display lesscluttered.

E Select Hide to suppress the display of the summary statistics column label.

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E Right-click Gender on the canvas pane and deselect (uncheck) Show Variable Label.

E Now click OK to create the table.

Figure 4-14Crosstabulation with demographic variables nested in columns

Layers

You can use layers to add a dimension of depth to your tables, creatingthree-dimensional “cubes.” Layers are, in fact, quite similar to nesting or stacking; theprimary difference is that only one layer category is visible at a time. For example,using Age category as the row variable and Gender as a layer variable produces a tablein which information for males and females is displayed in different layers of the table.




E Click Layers at the top of the table builder to display the Layers list.

E Drag and drop Gender from the variable list to the Layers list.

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Figure 4-15Age category in rows, Gender in layers

At this point, you might notice that adding a layer variable has no visible effect onthe preview displayed on the canvas pane. Layer variables do not affect the previewon the canvas pane unless the layer variable is the statistics source variable and youchange the summary statistics.


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Figure 4-16Simple layered table

At first glance, this table doesn’t look any different than a simple table of a singlecategorical variable. The only difference is the presence of the label Gender Male atthe top of the table.

E Double-click the table in the Viewer window to activate it.

E You can now see that the label Gender Male is actually a choice in a drop-down list.

E Click the down arrow on the drop-down list to display the whole list of layers.

Figure 4-17List of layers in activated pivot table

In this table, there is only one other choice in the list.

E Select Gender Female from the drop-down list.

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Figure 4-18Simple layered table with different layer displayed

Two Stacked Categorical Layer Variables

If you have more than one categorical variable in the layers, you can either stack ornest the layer variables. By default, layer variables are stacked. (Note: If you have anyscale layer variables, layer variables can only be stacked.)


E If you don’t already have Age category in the rows and Gender in the layers, follow thedirections above for creating a layered table.

E Drag and drop Highest degree from the variable list to the Layer list below Gender.

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Figure 4-19Stacked layer variables in table builder

The two radio buttons below the Layer list in the Layer Output group are now activated.The default selection is Show each category as a layer. This is equivalent to stacking.




Figure 4-20List of stacked layers in activated pivot table

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There are seven layers in the table: two layers for the two Gender categories and fivelayers for the five Highest degree categories. For stacked layers, the total number oflayers is the sum of the number of categories for the layer variables (including any totalor subtotal categories you have requested for the layer variables).

Two Nested Categorical Layer Variables

Nesting categorical layer variables creates a separate layer for each combination oflayer variable categories.


E If you haven’t done so already, follow the directions above for creating a table ofstacked layers.

E In the Layer Output group, select Show each combination of categories as a layer. Thisis equivalent to nesting.




Figure 4-21List of nested layers in activated pivot table

There are 10 layers in the table (you have to scroll through the list to see all of them),one for each combination of Gender and Highest degree. For nested layers, the totalnumber of layers is the product of the number of categories for each layer variable (inthis example, 5 x 2 = 10).

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Printing Layered Tables

By default, only the currently visible layer is printed. To print all layers of a table:


E From the Viewer window menus, choose:Format

Table Properties...

E Click the Printing tab.

E Select Print all layers.

You can also save this setting as part of a TableLook, including the default TableLook.

Chapter

5Totals and Subtotals forCategorical Variables

You can include both totals and subtotals in custom tables. Totals and subtotals can beapplied to categorical variables at any nesting level in any dimension—row, column,or layer.

Sample Data File



Simple Total for a Single VariableE From the menus, choose:

AnalyzeTables

Custom Tables...


E Right-click Age category on the canvas pane and choose Summary Statistics fromthe pop-up context menu.


E In the Label cell in the Display list, delete the default label and type Percent.

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E Click Continue.

E Right-click Age category on the canvas pane and choose Categories and Totals fromthe pop-up context menu.




Figure 5-2Simple total for a single categorical variable

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Totals and Subtotals for Categorical Variables

What You See Is What Gets TotaledTotals are based on categories displayed in the table. If you choose to excludesome categories from a table, cases from those categories are not included in totalcalculations.





E Click the category labeled 65 or older in the Label list.

E Click the arrow key to the left of the Exclude list again.

The two categories are moved from the Display list to the Exclude list.

Figure 5-3Manually excluded categories

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Figure 5-4Total in table with excluded categories

The total count in this table is only 2,107, compared to 2,828 when all of the categoriesare included. Only the categories that are used in the table are included in the total.(The percentage total is still 100% because all of the percentages are based on the totalnumber of cases used in the table, not the total number of cases in the data file.)

Display Position of TotalsBy default, totals are displayed below the categories being totaled. You can change thedisplay position of totals to show them above the categories being totaled.



E In the Totals and Subgroups Appear group, select Above.


Figure 5-5Total displayed above totaled categories

Totals for Nested TablesSince totals can be applied to categorical variables at any level of the nesting, you cancreate tables that contain group totals at multiple nesting levels.

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Group Totals

Totals for categorical variables nested within other categorical variables representgroup totals.


E Drag and drop Gender to the left of Age category on the canvas pane.


Before creating the table, let’s move the totals back below the totaled categories.

E In the Totals and Subgroups Appear group, select Below.

E Click Apply to save the setting and return to the table builder.

Figure 5-6Age category nested within Gender in the table builder


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Figure 5-7Age category totals within Gender categories

The table now displays two group totals: one for males and one for females.

Grand Totals

Totals applied to nested variables are always group totals, not grand totals. If youwant totals for the entire table, you can apply totals to the variable at the outermostnesting level.


E Right-click Gender on the canvas pane and choose Categories and Totals from thepop-up context menu.



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Figure 5-8Grand totals for a nested table

Notice that the grand total is only 2,107, not 2,828. Two age categories are stillexcluded from the table, so the cases in those categories are excluded from all totals.

Layer Variable Totals

Totals for layer variables are displayed as separate layers in the table.


E Click Layers in the table builder to display the Layers list.

E Drag and drop Gender from the row area on the canvas pane to the Layers list.

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Figure 5-9Layer variable in table builder

Note: Since you already specified totals for Gender, you don’t need to do so now.Moving the variable between dimensions does not affect any of the settings for thatvariable.


E Double-click the table in the Viewer to activate it.

E Click the down arrow in the Layer drop-down list to display a list of all the layers inthe table.

There are three layers in the table: Gender Male, Gender Female, and Gender Total.

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Figure 5-10Total layer in Layer list in activated pivot table

Display Position of Layer Totals

For layer variable totals, the display position (above or below) for totals determinesthe layer position for the totals. For example, if you specify Above for a layer variabletotal, the total layer is the first layer displayed.

SubtotalsYou can include subtotals for subsets of categories of a variable. For example, youcould include subtotals for age categories that represent all of the respondents in thesample survey under and over age 45.


E Click Reset to clear any previous settings in the table builder.



E Select 3.00 in the Value(s) list.

E In the Label text field next to the Insert button, type Subtotal < 45.

E Then click Insert.



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E In the Label text field next to the Insert button, type Subtotal 45+.


Figure 5-11Defining subtotals

Important note: You should select the display position for totals and subtotals (Above

or Below) before defining any subtotals. Changing the display position affects allsubtotals (not just the currently selected subtotal), and it also changes the categoriesincluded in the subtotals.


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Figure 5-12Subtotals for Age category

What You See Is What Gets Subtotaled

Just like totals, subtotals are based on the categories included in the table.



Note: The value (not the label) displayed for the first subtotal is 1.00...3.00, indicatingthat the subtotal includes all of the values in the list between 1 and 3.

E Select 1.00 in the Value(s) list (or click the label Less than 25).


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Figure 5-13Subtotals when categories are excluded

The first age category is now excluded, and the value displayed for the first subtotalchanges to 2.00...3.00, indicating the fact that the excluded category will not be includedin the subtotal because subtotals are based on the categories included in the table.Excluding a category automatically excludes it from any subtotals, so you cannot, forexample, display only subtotals without the categories on which the subtotals are based.

Hiding Subtotaled Categories

You can suppress the display of the categories that define a subtotal and display onlythe subtotal, effectively “collapsing” categories without affecting the underlying data.




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E In the Label text field next to the Insert button, type Less than 45.

E Select (check) Hide subtotaled categories.




E In the Label text field next to the Insert button, type 45 or older.

E Select (check) Hide subtotaled categories.


E To include a total with the subtotals, select (check) Total at the bottom of the dialog box.

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Figure 5-14Hiding subtotaled categories

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E Click Apply.

The canvas reflects the fact that subtotals will be displayed but the categories thatdefine the subtotals will be excluded.

Figure 5-15Canvas displaying subtotals without subtotaled categories

E Click OK to produce the table.

Figure 5-16Table displaying only subtotals and totals

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Layer Variable Subtotals

Just like totals, subtotals for layer variables are displayed as separate layers in thetable. Essentially, the subtotals are treated as categories. Each category is a separatelayer in the table, and the display order of the layer categories is determined by thecategory order specified in the Categories and Totals dialog box, including the displayposition of the subtotal categories.

Chapter

6Tables for Variables with SharedCategories

Surveys often contain many questions with a common set of possible responses. Forexample, our sample survey contains a number of variables concerning confidence invarious public and private institutions and services, all with the same set of responsecategories: 1 = A great deal, 2 = Only some, and 3 = Hardly any. You can use stackingto display these related variables in the same table—and you can display the sharedresponse categories in the columns of the table.

Figure 6-1Table of variables with shared categories

Note: In the previous version of Custom Tables, this was known as a “table offrequencies.”

Sample Data File


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Table of CountsE From the menus, choose:

AnalyzeTables

Custom Tables...

E In the variable list in the table builder, click Confidence in banks... and then Shift-clickConfidence in television to select all of the “confidence” variables. (Note: Thisassumes that variable labels are displayed in alphabetical order, not file order, in thevariable list.)

E Drag and drop the six confidence variables to the Rows area on the canvas pane.

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Figure 6-2Confidence variables stacked in rows

This stacks the variables in the row dimension. By default, the category labels foreach variable are also displayed in the rows, resulting in a very long, narrow table (6variables x 3 categories = 18 rows)—but since all six variables share the same definedcategory labels (value labels), you can put the category labels in the column dimension.

E From the Category Position drop-down list, select Row Labels in Columns.

Now the table has only six rows, one for each of the stacked variables, and the definedcategories become columns in the table.

E Before creating the table, select (click) Hide for Position in the Summary Statisticsgroup, since the summary statistic label Count isn’t really necessary.

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Figure 6-3Category labels in columns


Figure 6-4Table of stacked row variables with shared category labels in columns

Instead of displaying the variables in the rows and categories in the columns, you couldcreate a table with the variables stacked in the columns and the categories displayed inthe rows. This might be a better choice if there were more categories than variables,whereas in our example there are more variables than categories.

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Table of Percentages

For a table with variables stacked in rows and categories displayed in columns, themost meaningful (or at least easiest to understand) percentage to display is rowpercentages. (For a table with variables stacked in the columns and categoriesdisplayed in the rows, you would probably want column percentages.)


E Right-click any one of the confidence variables in the table preview on the canvas paneand choose Summary Statistics from the pop-up context menu.

E Select Row N % in the Statistics list and click the arrow button to move it to the Displaylist.

E Click any cell in the Count row in the Display list and click the arrow button to move itback to the Statistics list, removing it from the Display list.

Figure 6-5Row percentages selected

E Click Apply to All to apply the summary statistic change to all of the stacked variables inthe table.

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Figure 6-6Row percentages in table preview on canvas pane

Note: If your table preview doesn’t look like this figure, you probably clicked Apply to

Selection instead of Apply to All, which applies the new summary statistic only to theselected variable. In this example, that would result in two columns for each category:one with count placeholders displayed for all of the other variables and one with a rowpercentage placeholder displayed for the selected variable. This is exactly the tablethat would be produced but not the one that we want in this example.


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Figure 6-7Table of row percentages for variables stacked in rows, categories displayed in columns

Note: You can include any number of summary statistics in a table of variables withshared categories. Our examples show only one at a time to keep them simple.

Totals and Category Control

You can create tables with categories in the opposite dimension from the variables onlyif all of the variables in the table have the same categories, displayed in the same order.This includes totals, subtotals, and any other category adjustments you make. Thismeans that any modifications you make in the Categories and Totals dialog box mustbe made for all variables in the table that share the categories.


E Right-click the first confidence variable in the table preview on the canvas pane andchoose Categories and Totals from the pop-up context menu.

E Select (check) Total in the Categories and Totals dialog box and then click Apply.

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Figure 6-8Probably not the results you want

The first thing you’ll probably notice is that the category labels have moved from thecolumns back to the rows. You may also notice that the Category Position control isnow disabled. This is because the variables no longer share the exact same set of“categories.” One of the variables now has a total category.

E Right-click any one of the confidence variables on the canvas pane and select Select All

Row Variables from the pop-up context menu—or Ctrl-click each stacked variable onthe canvas pane until they are all selected (you may have to scroll down the pane orexpand the table builder window).

E Click Categories and Totals in the Define group.

E If Total isn’t already selected (checked) in the Categories and Totals dialog box, selectit now and then click Apply.

E The Category Position drop-down list should be enabled again, since now all of thevariables have the additional total category, so select Row Labels in Columns.

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Figure 6-9Categories and totals in columns


Figure 6-10Table of row percentages for variables stacked in rows, categories and totals displayed incolumns

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Nesting in Tables with Shared Categories

In nested tables, the stacked variables with the shared categories must be at theinnermost nesting level of their dimension if you want to display the category labels inthe opposite dimension.


E Drag and drop Gender from the variable list to the left side of the Rows area.

Figure 6-11Nested variables with shared categories

The stacked variables with shared categories are now nested within gender categoriesin the table preview.

E Now drag and drop Gender to the right of one of the stacked confidence variables inthe table preview.

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Figure 6-12Another example of results you probably do not want

Once again, the category labels have reverted back to the row dimension, and theCategory Position control is disabled. You now have one stacked variable that also hasGender nested within it, while the other stacked variables contain no nested variables.You could add Gender as a nested variable to each of the stacked variables, but thenmoving row labels to columns would result in the category labels for Gender beingdisplayed in the columns, not the category labels for the stacked variables with theshared categories. This is because Gender would now be the innermost nested variable,and changing the category position always applies to the innermost nested variable.

Chapter

7Summary Statistics

Summary statistics include everything from simple counts for categorical variables tomeasures of dispersion, such as the standard error of the mean for scale variables. Itdoes not include significance tests available on the Test Statistics tab in the CustomTables dialog box. For more information, see “Test Statistics” in Chapter 9 on p. 155.

Summary statistics for categorical variables and multiple response sets include countsand a wide variety of percentage calculations, including:

Row percentages

Column percentages

Subtable percentages

Table percentages

Valid N percentages

In addition to the summary statistics available for categorical variables, summarystatistics for scale variables and custom total summaries for categorical variablesinclude:

Mean

Median

Percentiles

Sum

Standard deviation

Range

Minimum and maximum values

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Additional summary statistics are available for multiple response sets. For moreinformation, see “Counts, Responses, Percentages, and Totals” in Chapter 10 on p.175. A complete list of summary statistics is also available. For more information, see“Summary Statistics” in Chapter 2 on p. 37.

Sample Data File



Summary Statistics Source Variable

Available summary statistics depend on the measurement level of the summarystatistics source variable. The source of summary statistics (the variable on which thesummary statistics are based) is determined by:

Measurement level. If a table (or a table section in a stacked table) contains a scalevariable, summary statistics are based on the scale variable.

Variable selection order. The default statistics source dimension (row or column)for categorical variables is based on the order in which you drag and drop variablesonto the canvas pane. For example, if you drag a variable to the rows area first, therow dimension is the default statistics source dimension.

Nesting. For categorical variables, summary statistics are based on the innermostvariable in the statistics source dimension.

A stacked table may have multiple summary statistics source variables (both scale andcategorical), but each table section has only one summary statistics source.

Summary Statistics Source for Categorical Variables



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E In the table builder, drag and drop Age category from the variable list into the Rowsarea of the canvas pane.

E Right-click on Age category on the canvas pane and select Summary Statistics from thepop-up context menu. (Since this is the only variable in the table, it is the statisticssource variable.)

E In the Summary Statistics dialog box, select Column N % in the Statistics list andclick the arrow to add it to the Display list.

Figure 7-1Summary Statistics dialog box for categorical variables

E Click Apply to Selection.

E In the table builder, drag and drop Get news from internet to the right of Age categoryon the canvas pane.

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Figure 7-2Nested categorical variables

E Right-click on Age category on the canvas pane again. The Summary Statistics item onthe context menu is now disabled because Age category is not the innermost nestedvariable in the statistics source dimension.

E Right-click on Get news from internet on the canvas pane. The Summary Statistics itemis enabled because it is now the summary statistics source variable, since it is theinnermost nested variable in the statistics source dimension. (Since the table has onlyone dimension—rows—it is the statistics source dimension.)

E Drag and drop Get news from internet from the Rows area on the canvas pane intothe Columns area.

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Figure 7-3Crosstabulated categorical variables

E Right-click on Get news from internet on the canvas pane again. The Summary Statistics

item on the pop-up context menu is now disabled because the variable is no longer inthe statistics source dimension.

Age category is once again the statistics source variable because the default statisticssource dimension for categorical variables is the first dimension where you putvariables when creating the table. In this example, the first thing we did was putvariables in the row dimension. Thus, the row dimension is the default statistics sourcedimension; and since Age category is now the only variable in that dimension, it is thestatistics source variable.

Summary Statistics Source for Scale Variables

E Drag and drop the scale variable Hours per day watching TV to the left of Age categoryin the Rows area of the canvas pane.

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Figure 7-4Crosstabulation with scale summary statistics variable

The first thing you may notice is that the Count and Column N % summaries havebeen replaced with Mean—and if you right-click on Hours per day watching TV onthe canvas pane, you’ll see that it is now the summary statistics source variable. For atable with a scale variable, the scale variable is always the statistics source variableregardless of its nesting level or dimension, and the default summary statistic forscale variables is the mean.

E Drag and drop Hours per day watching TV from the Rows area into the Columns areaabove Get news from internet.

E Right-click on Hours per day watching TV and select Summary Statistics from thepop-up context menu. (It’s still the statistics source variable even when you move it toa different dimension.)

E In the Summary Statistics dialog box, click the Format cell for the mean in the Displaylist and select nnnn from the Format drop-down list. (You may have to scroll up thelist to find this choice.)

E In the Decimals cell, type 2.

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Figure 7-6Scale summary statistic with two decimals

The table preview on the canvas pane now shows that the mean values will be displayedwith two decimals.


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Figure 7-7Scale variable summarized within crosstabulated categorical variables

Stacked Variables

Since a stacked table can contain multiple statistics source variables and you canspecify different summary statistics for each of those statistics source variables, thereare a few special considerations for specifying summary statistics in stacked tables.



E Click Get news from internet in the variable list and then shift-click Get news fromtelevision in the variable list to select all of the “news” variables. (Note: This assumesthat variable labels are displayed in alphabetical order, not file order, in the variablelist.)

E Drag and drop the five news variables into the Rows area of the canvas pane.

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Figure 7-8News variables stacked in rows

The five news variables are stacked in the row dimension.

E Click Get news from internet on the canvas pane so that only that variable is selected.

E Now right-click Get news from internet and select Summary Statistics from the pop-upcontext menu.

E In the Summary Statistics dialog box, select Column N % from the Statistics list andclick the arrow to add it to the Display list. (You can use the arrow to move selectedstatistics from the Statistics list into the Display list, or you can drag and drop selectedstatistics from the Statistics list into the Display list.)

E Then click Apply to Selection.

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Figure 7-9Additional statistic applied to one variable in a stacked table

A column is added for column percentages—but the table preview on the canvas paneindicates that column percentages will be displayed for only one variable. This isbecause in a stacked table there are multiple statistics source variables, and each onecan have different summary statistics. In this example, however, we want to display thesame summary statistics for all variables.

E Right-click Get news from newspapers on the canvas pane and select Summary Statistics

from the pop-up context menu.

E In the Summary Statistics dialog box, select Column N % from the Statistics list andclick the arrow to add it to the Display list.

E Then click Apply to All.

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Figure 7-10Additional statistic applied to all variables in a stacked table

Now the table preview indicates that column percentages will be displayed for allof the stacked variables.

Custom Total Summary Statistics for Categorical Variables

For categorical statistics source variables, you can include custom total summarystatistics that are different from the statistics displayed for the categories of thevariable. For example, for an ordinal variable, you could display percentages for eachcategory and the mean or median for the custom total summary statistic.



E Click Confidence in press in the variable list, and then Ctrl-click Confidence in TVto select both variables.

E Drag and drop the two variables into the Rows area of the canvas pane. This stacksthe two variables in the row dimension.

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E Right-click either variable on the canvas pane and select Select All Row Variables fromthe pop-up context menu. (They may both already be selected, but we want to makesure.)

E Right-click the variable again and select Categories and Totals from the pop-up contextmenu.

E In the Categories and Totals dialog box, click (check) Total, and then click Apply.

The table preview on the canvas pane now displays a total row for both variables.In order to display custom total summary statistics, totals and/or subtotals must bespecified for the table.

E Right-click either variable on the canvas pane and select Summary Statistics from thepop-up context menu.

E In the Summary Statistics dialog box, click Count in the Display list and click thearrow to move it to the Statistics list, removing it from the Display list.

E Click Column N % in the Statistics list and click the arrow key to move it to theDisplay list.

E Click (check) Custom Summary Statistics for Totals and Subtotals.

E Click Count in the custom summary Display list and click the arrow to move it to thecustom summary Statistics list, removing it from the Display list.

E Click Mean in the custom summary Statistics list and click the arrow to move it to thecustom summary Display list.

E Click the Format cell for the mean in the Display list and select nnnn from thedrop-down list of formats. (You may have to scroll up the list to find this choice.)


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Figure 7-11Selecting custom summary statistics for totals

E Click Apply to All to apply these settings to both variables in the table.

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Figure 7-12Custom total summary statistics for row variables displayed in columns

A new column has been added for the custom total summary statistic, which may notbe what you want, since the preview on the canvas pane clearly indicates that this willresult in a table with many empty cells.

E In the table builder, in the Summary Statistics group, select Rows from the Positiondrop-down list.

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Figure 7-13Summary statistics for row variables displayed in rows

This moves all the summary statistics to the row dimension, displaying all summarystatistics in a single column in the table.


Figure 7-14Categorical variables with custom total summary statistics

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Displaying Category Values

There’s only one small problem with the preceding table—it may be hard to interpretthe mean value without knowing the underlying category values on which it is based.Is a mean of 2.34 somewhere between A great deal and Only some—or is it somewherebetween Only some and Hardly any?

Although we can’t address this problem directly in Custom Tables, we can addressit in a more general way.

E From the menus, choose:Edit

Options...

E In the Options dialog box, click the Output Labels tab.

E In the Pivot Table Labeling group, select Values and Labels from the Variable values in

labels shown as drop-down list.

Figure 7-15Output labeling options

E Click OK to save this setting.

E Open the table builder (Analyze menu, Tables, Custom Tables) and click OK to createthe table again.

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Figure 7-16Values and labels displayed for variable categories

The category values make it clear that a mean of 2.34 is somewhere between Onlysome and Hardly any. Displaying the category values in the table makes it much easierto interpret the value of custom total summary statistics, such as the mean.

This display setting is a global setting that affects all pivot table output from allprocedures and persists across sessions until you change it. To change the settingback to display only value labels:


Options...

E In the Options dialog box, click the Output Labels tab.

E In the Pivot Table Labeling group, select Labels from the Variable values in labels shown

as drop-down list.

E Click OK to save this setting.

Chapter

8Summarizing Scale Variables

A wide range of summary statistics are available for scale variables. In addition to thecounts and percentages available for categorical variables, summary statistics for scalevariables also include:

Mean

Median

Percentiles

Sum

Standard deviation

Range

Minimum and maximum values

For more information, see “Summary Statistics for Scale Variables and CategoricalCustom Totals” in Chapter 2 on p. 42.

Sample Data File


All examples provided here display variable labels in dialog boxes, sorted inalphabetical order. Variable list display properties are specified on the General tab inthe Options dialog box (Edit menu, Options).

Stacked Scale Variables

You can summarize multiple scale variables in the same table by stacking them inthe table.

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E In the table builder, click Age of respondent in the variable list, Ctrl-click Highest yearof school completed, and Ctrl-click Hours per day watching TV to select all threevariables.

E Drag and drop the three selected variables to the Rows area of the canvas pane.

Figure 8-1Stacked scale variables in table builder

The three variables are stacked in the row dimension. Since all three variables are scalevariables, no categories are displayed, and the default summary statistic is the mean.


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Figure 8-2Table of mean values of stacked scale variables

Multiple Summary Statistics

By default, the mean is displayed for scale variables; however, you can choose othersummary statistics for scale variables, and you can display more than one summarystatistic.


E Right-click any one of the three scale variables in the table preview on the canvas paneand select Summary Statistics from the pop-up context menu.

E In the Summary Statistics dialog box, select Median in the Statistics list and click thearrow to add it to the Display list. (You can use the arrow to move selected statisticsfrom the Statistics list to the Display list, or you can drag and drop selected statisticsfrom the Statistics list into the Display list.)

E Click the Format cell for the median in the Display list and select nnnn from thedrop-down list of formats.


E Make the same changes for the mean in the Display list.

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Figure 8-3Mean and median selected in Summary Statistics dialog box

E Click Apply to All to apply these changes to all three scale variables.

E Click OK in the table builder to create the table.

Figure 8-4Mean and median displayed in table of stacked scale variables

Count, Valid N, and Missing ValuesIt is often useful to display the number of cases used to compute summary statistics,such as the mean, and you might assume (not unreasonably) that the summary statisticCount would provide that information. However, this will not give you an accuratecase base if there are any missing values. To obtain an accurate case base, use Valid N.



E In the Summary Statistics dialog box, select Count in the Statistics list and click thearrow to add it to the Display list.

E Then select Valid N in the Statistics list and click the arrow to add it to the Display list.


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Figure 8-5Count versus Valid N

For all three variables, Count is the same: 2,832. Not coincidentally, this is the totalnumber of cases in the data file. Since the scale variables aren’t nested within anycategorical variables, Count simply represents the total number of cases in the data file.

Valid N, on the other hand, is different for each variable and differs quite a lot fromCount for Hours per day watching TV. This is because there is a large number ofmissing values for this variable—that is, cases with no value recorded for this variableor values defined as representing missing data (such as a code of 99 to represent NotApplicable for pregnancy in males).



E In the Summary Statistics dialog box, select Valid N in the Display list and click thearrow key to move it back to the Statistics list, removing it from the Display list.

E Select Count in the Display list and click the arrow key to move it back to the Statisticslist, removing it from the Display list.

E Select Missing in the Statistics list and click the arrow key to add it to the Display list.



Figure 8-6Number of missing values displayed in table of scale summary statistics

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The table now displays the number of missing values for each scale variable. Thismakes it quite apparent that Hours per day watching TV has a large number of missingvalues, whereas the other two variables have very few. This may be a factor to considerbefore putting a great deal of faith in the summary values for that variable.

Different Summaries for Different Variables

In addition to displaying multiple summary statistics, you can display differentsummary statistics for different scale variables in a stacked table. For example, theprevious table revealed that only one of the three variables has a large number ofmissing values; so you might want to show the number of missing values for onlythat one variable.


E Click Age of respondent in the table preview on the canvas pane, and then Ctrl-clickHighest year of school completed to select both variables.

E Right-click either of the two selected variables and select Summary Statistics fromthe pop-up context menu.

E In the Summary Statistics dialog box, select Missing in the Display list and click thearrow key to move it back to the Statistics list, removing it from the Display list.

E Click Apply to Selection to apply the change to only the two selected variables.

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Figure 8-7Table preview for different summary statistics for different variables

The placeholders in the data cells of the table indicate that the number of missingvalues will be displayed only for Hours per day watching TV.


Figure 8-8Table of different summary statistics for different variables

Although this table provides the information that we want, the layout may make itdifficult to interpret the table. Somebody reading the table might think that the blankcells in the Missing column indicate zero missing values for those variables.


E In the Summary Statistics group in the table builder, select Rows from the Positiondrop-down list.

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Figure 8-9Moving summary statistics from the column dimension to the row dimension


Figure 8-10Summary statistics and variables both displayed in the row dimension

Now it’s clear that the table reports the number of missing values for only one variable.

Group Summaries in Categories

You can use categorical variables as grouping variables to display scale variablesummaries within groups defined by the categories of the categorical variable.


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E Drag and drop Gender from the variable list into the Columns area of the canvas pane.

If you right-click Gender in the table preview on the canvas pane, you will see thatSummary Statistics is disabled on the pop-up context menu. This is because in a tablewith scale variables, the scale variables are always the statistics source variables.


Figure 8-11Grouped scale summaries using a categorical column variable

This table makes it easy to compare the averages (mean and median) for males andfemales, and it clearly shows that there isn’t much difference between them—whichmay not be terribly interesting but might be useful information.

Multiple Grouping Variables

You can subdivide the groups further by nesting and/or using both row and columncategorical grouping variables.


E Drag and drop Get news from internet from the variable list to the far left side of theRows area of the canvas pane. Make sure to position it so that all three scale variablesare nested within it, not just one of them.

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Figure 8-12Correct: All three scale variables nested within the categorical variable

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Figure 8-13Wrong: Only one scale variable nested within the categorical variable

Although there may be times when you want something like the second exampleabove, it’s not what we want in this case.


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Figure 8-14Scale summaries grouped by categorical row and column variables

Nesting Categorical Variables within Scale Variables

Although the above table may provide the information you want, it may not provide itin the easiest format to interpret. For example, you can compare the average age ofmen who use the Internet to get news and those who don’t—but it would be easier todo if the values were next to each other rather than separated. Swapping the positionsof the two row variables and nesting the categorical grouping variable within the threescale variables might improve the table. With scale variables, nesting level has noeffect on the statistics source variable. The scale variable is always the statistics sourcevariable regardless of nesting level.


E Click Age of respondent in the table preview on the canvas pane, Ctrl-click Highestyear of school completed, and Ctrl-click Hours per day watching TV to select all threescale variables.

E Drag and drop the three scale variables onto the far left side of the Rows area, nestingthe categorical variable Get news from internet within each of the three scale variables.


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Figure 8-15Categorical row variable nested within stacked scale variables

The choice of nesting order depends on the relationships or comparisons that youwant to emphasize in the table. Changing the nesting order of the scale variablesdoesn’t change the summary statistics values; it changes only their relative positions inthe table.

Chapter

9Test Statistics

Three different tests of significance are available for studying the relationship betweenrow and column variables. This chapter discusses the output of each of these tests,with special attention to the effects of nesting and stacking. For more information, see“Stacking, Nesting, and Layers with Categorical Variables” in Chapter 4 on p. 77.

Sample Data File


Tests of Independence (Chi-Square)

The chi-square test of independence is used to determine whether there is a relationshipbetween two categorical variables. For example, you may want to determine whetherLabor force status is related to Marital status.



E In the table builder, drag and drop Labor force status from the variable list into theRows area of the canvas pane.

E Drag and drop Marital status from the variable list into the Columns area.

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Figure 9-1Variables displayed on canvas pane

E Select Rows as the position for the summary statistics.

E Select Labor force status and click Summary Statistics in the Define group.


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E Select Column N % in the Statistics list and add it to the Display list.


E In the Custom Tables dialog box, click the Test Statistics tab.

Figure 9-3Test Statistics tab with the Tests of independence (chi-square) selected

E Select Tests of independence (Chi-square).

E Click OK to create the table and obtain the chi-square test.

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Figure 9-4Labor force status by Marital status

This table is a crosstabulation of Labor force status by Marital status, with countsand column proportions shown as the summary statistics. Column proportions arecomputed so that they sum to 100% down each column. If these two variables areunrelated, then in each row the proportions should be similar across columns. Thereappear to be differences in the proportions, but you can check the chi-square test to besure.

Figure 9-5Pearson’s chi-square test

The test of independence hypothesizes that Labor force status and Marital statusare unrelated—that is, that the column proportions are the same across columns,and any observed discrepancies are due to chance variation. The chi-square statisticmeasures the overall discrepancy between the observed cell counts and the countsyou would expect if the column proportions were the same across columns. A larger

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chi-square statistic indicates a greater discrepancy between the observed and expectedcell counts—greater evidence that the column proportions are not equal, that thehypothesis of independence is incorrect, and, therefore, that Labor force status andMarital status are related.

The computed chi-square statistic has a value of 729.242. In order to determinewhether this is enough evidence to reject the hypothesis of independence, thesignificance value of the statistic is computed. The significance value is the probabilitythat a random variate drawn from a chi-square distribution with 28 degrees of freedomis greater than 729.242. Since this value is less than the alpha level specified on theTest Statistics tab, you can reject the hypothesis of independence at the 0.05 level.Thus, Labor force status and Marital status are in fact related.

Effects of Nesting and Stacking on Tests of Independence

The rule for tests of independence is as follows: a separate test is performed foreach innermost subtable. To see how nesting affects the tests, consider the previousexample, but with Marital status nested within levels of Gender.


E Drag and drop Gender from the variable list into the Columns area of the canvas paneabove Marital status.



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With Marital status nested within levels of Gender, two tests are performed—one foreach level of Gender. The significance value for each test indicates that you can rejectthe hypothesis of independence between Marital status and Labor force status for bothmales and females. However, the table notes that more than 20% of each table’s cellshave expected counts of less than 5, and the minimum expected cell count is lessthan 1. These notes indicate that the assumptions of the chi-square test may not bemet by these tables, so the results of the tests are suspect. The notes in this figureare numbered rather than lettered because this figure is formatted using the SPSSDoc TableLook instead of the default.

Note: The footnotes may be cut off from view by the cell boundaries. You can makethem visible by changing the alignment of these cells in the Cell Properties dialog box.

To see how stacking affects the tests:


E Drag and drop Highest degree from the variable list into the Rows area below Laborforce status.



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With Highest degree stacked with Labor force status, four tests are performed—a testof the independence of Marital status and Labor force status, and a test of Maritalstatus and Highest degree for each level of Gender. The test results for Marital statusand Labor force status are the same as before. The test results for Marital status andHighest degree indicate these variables are not independent.

Comparing Column Means

The column means tests are used to determine whether there is a relationship between acategorical variable in the Columns and a continuous variable in the Rows. Moreover,you can use the test results to determine the relative ordering of categories of thecategorical variable in terms of the mean value of the continuous variable. Forexample, you may want to determine whether Hours per day watching TV is relatedto Get news from newspapers.



E Click Reset to restore the default settings to all tabs.

E In the table builder, drag and drop Hours per day watching TV from the variable listinto the Rows area of the canvas pane.

E Drag and drop Get news from newspapers from the variable list into the Columns area.

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E Select Hours per day watching TV and click Summary Statistics in the Define group.


E Select nnnn as the format.

E Select 2 as the number of decimals to display. Notice that this causes the format tonow read nnnn.nn.


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Figure 9-10Test Statistics tab with Compare column means (t tests) selected

E Select Compare column means (t-tests).

E Click OK to create the table and obtain the column means tests.

Figure 9-11Get news from newspapers by Hours per day watching TV

This table shows the mean Hours per day watching TV for people who do and do notget their news from newspapers. The observed difference in these means suggeststhat people who do not get their news from newspapers spend approximately 0.18more hours watching TV than people who do get their news from newspapers. To seewhether this difference is due to chance variation, check the column means tests.

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Figure 9-12Comparisons of column means

The column means test table assigns a letter key to each category of the columnvariable. For Get news from newspapers, the category No is assigned the letter A, andYes is assigned the letter B. For each pair of columns, the column means are comparedusing a t test. Since there are only two columns, only one test is performed. For eachsignificant pair, the key of the category with the smaller mean is placed under thecategory with larger mean. Since no keys are reported in the cells of the table, thismeans that the column means are not statistically different.

Effects of Nesting and Stacking on Column Means Tests

The rule for column means tests is as follows: a separate set of pairwise tests isperformed for each innermost subtable. To see how nesting affects the tests, considerthe previous example, but with Hours per day watching TV nested within levels ofLabor force status.


E Drag and drop Labor force status from the variable list into the Rows area of thecanvas pane.


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With Hours per day watching TV nested within levels of Labor force status, seven setsof column means tests are performed: one for each level of Labor force status. Thesame letter keys are assigned to the categories of Get news from newspapers. Forrespondents working full time, the B key appears in the A column. This means thatfor full-time employees, the mean value of Hours per day watching TV is lower forpeople who get their news from newspapers. No other keys appear in the columns,so you can conclude that there are no other statistically significant differences in thecolumn means.

Bonferroni adjustments. When multiple tests are performed, the Bonferroni adjustmentis applied to column means tests to ensure that the alpha level (or false positive rate)specified on the Test Statistics tab applies to each set of tests. Thus, in this table,no Bonferroni adjustments were applied because although seven sets of tests areperformed, within each set only one pair of columns is compared.



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E Drag and drop Get news from internet from the variable list into the Columns area tothe left of Get news from newspapers.



With Get news from internet stacked with Get news from newspapers, 14 sets of columnmeans tests are performed—one for each level of Labor force status for Get news frominternet and Get news from newspapers. Again, no Bonferroni adjustments are appliedbecause within each set, only one pair of columns is compared. The tests for Get newsfrom newspapers are the same as before. For Get news from internet, the category Nois assigned the letter A and Yes is assigned the letter B. The B key is reported in the Acolumn for each set of column means tests except for those respondents temporarilynot working. This means that the mean value of Hours per day watching TV is lowerfor people who get their news from the Internet than for people who do not get theirnews from newspapers. No keys are reported for the Temporarily not working set; thus,the column means are not statistically different for these respondents.

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Comparing Column Proportions

The column proportions tests are used to determine the relative ordering of categoriesof the Columns categorical variable in terms of the category proportions of the Rowscategorical variable. For example, after using a chi-square test to find that Labor forcestatus and Marital status are not independent, you may want to see which rows andcolumns are responsible for this relationship.



E Click Reset to restore the default settings to all tabs.

E In the table builder, drag and drop Labor force status from the variable list into theRows area of the canvas pane.

E Drag and drop Marital status from the variable list into the Columns area.


E Select Labor force status and click Summary Statistics in the Define group.

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E Select Column N % in the Statistics list and add it to the Display list.

E Deselect Count from the Display list.



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Figure 9-17Test Statistics tab with Compare column proportions (z tests) selected

E Select Compare column proportions (z-tests).

E Click OK to create the table and obtain the column proportions tests.

Figure 9-18Labor force status by Marital status

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This table is a crosstabulation of Labor force status by Marital status, with columnproportions shown as the summary statistic.

Figure 9-19Comparisons of column proportions

The column proportions test table assigns a letter key to each category of the columnvariables. For Marital status, the category Married is assigned the letter A, Widowed isassigned the letter B, and so on, through the category Never married, which is assignedthe letter E. For each pair of columns, the column proportions are compared using a ztest. Seven sets of column proportions tests are performed, one for each level of Laborforce status. Since there are five levels of Marital status, (5*4)/2 = 10 pairs of columnsare compared in each set of tests, and Bonferroni adjustments are used to adjust thesignificance values. For each significant pair, the key of the smaller category is placedunder the category with the larger proportion.

For the set of tests associated with Working full time, the B key appears in each of theother columns. Also, the A key appears in the C column. No other keys are reported inother columns. Thus, you can conclude that the proportion of divorced persons whoare working full time is greater than the proportion of married persons working fulltime, which in turn is greater than the proportion of widowers working full time. Theproportions of people who are separated or never married and working full time cannotbe differentiated from people who are divorced or married and working full time, butthese proportions are greater than the proportion of widowers working full time.

For the tests associated with Working part time or School, the A, B, and C keysappear in the E column. No other keys are reported in other columns. Thus, theproportions of people who have never been married and are in school or are working

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part time are greater than the proportions of married, widowed, or divorced peoplewho are in school or working part time.

For the tests associated with Temporarily not working or with Other labor status, noother keys are reported in any columns. Thus, there is no discernible difference in theproportions of married, widowed, divorced, separated, or never-married people who aretemporarily not working or are in an otherwise uncategorized employment situation.

The tests associated with Retired show that the proportion of widowers who areretired is greater than the proportions of all other marital categories who are retired.Moreover, the proportions of married or divorced people who are retired is greater thanthe proportion of never-married persons who are retired.

There are greater proportions of people married, widowed, or separated and keepinghouse than proportions of people divorced or never married and keeping house.

The proportion of people who have never been married and are Unemployed, laid offis higher than the proportions of people who are married or widowed and unemployed.Also, note that the Separated column is marked with a “.”, which indicates that theobserved proportion of separated people in the Unemployed, laid off row is either 0or 1, and therefore no comparisons can be made using that column for unemployedrespondents.

Effects of Nesting and Stacking on Column Proportions Tests

The rule for column proportions tests is as follows: a separate set of pairwise tests isperformed for each innermost subtable. To see how nesting affects the tests, considerthe previous example, but with Labor force status nested within levels of Gender.


E Drag and drop Gender from the variable list into the Rows area of the canvas pane.


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With Labor force status nested within levels of Gender, 14 sets of column proportionstests are performed—one for each level of Labor force status for each level of Gender.The same letter keys are assigned to the categories of Marital status.

There are a couple of things to note about the table results:

With more tests, there are more columns with zero column proportion. They aremost common among separated respondents and widowed males.

The column differences previously seen among respondents keeping house seemsto be entirely due to females.



E Drag and drop Highest degree from the variable list into the Rows area below Gender.

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With Highest degree stacked with Gender, 19 sets of column means tests areperformed—the 14 previously discussed plus one for each level of Highest degree. Thesame letter keys are assigned to the categories of Marital status.

There are a few things to note about the table results:

The test results for the 14 previously run sets of tests are the same.

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People who have less than a high school degree are more common amongwidowers than among married, divorced, or never-married respondents.

People with some post-high school education tend to be more common amongthose people who are married, divorced, and never married than among widowers.

A Note on Weights and Multiple Response Sets

Case weights are always based on counts, not responses, even when one of thevariables is a multiple response variable.

Chapter

10Multiple Response Sets

Custom Tables and the Chart Builder support a special kind of “variable” called amultiple response set. Multiple response sets aren’t really “variables” in the normalsense. You can’t see them in the Data Editor, and other procedures don’t recognizethem. Multiple response sets use multiple variables to record responses to questionswhere the respondent can give more than one answer. Multiple response sets aretreated like categorical variables, and most of the things you can do with categoricalvariables, you can also do with multiple response sets.

Multiple response sets are constructed from multiple variables in the data file.A multiple response set is a special construct within an SPSS-format data file. Youcan define and save multiple sets in an SPSS-format data file, but you cannot importor export multiple response sets from/to other file formats. (You can copy multipleresponse sets from other SPSS data files using Copy Data Properties, which is accessedfrom the Data menu in the Data Editor window. )

Sample Data File


All examples provided here display variable labels in dialog boxes, sorted inalphabetical order. Variable list display properties are specified on the General tab inthe Options dialog box (Edit menu, Options).

Counts, Responses, Percentages, and Totals

All of the summary statistics available for categorical variables are also availablefor multiple response sets. Some additional statistics are also available for multipleresponse sets.

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E Drag and drop News sources (this is the descriptive label for the multiple response set$mltnews) from the variable list into the Rows area of the canvas pane.

The icon next to the “variable” in the variable list identifies it as a multiple dichotomyset.

Figure 10-1Multiple dichotomy set icon

Figure 10-2Multiple dichotomy set displayed in table preview

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For a multiple dichotomy set, each “category” is, in fact, a separate variable, and thecategory labels are the variable labels (or variable names for variables without definedvariable labels). In this example, the counts that will be displayed represent the numberof cases with a Yes response for each variable in the set.

E Right-click News sources in the table preview on the canvas pane and select Categories

and Totals from the pop-up context menu.

E Select (click) Total in the Categories and Totals dialog box, and then click Apply.

E Right-click News sources again and select Summary Statistics from the pop-up contextmenu.


E Click Apply to Selection, and then click OK to create the table.

Figure 10-3Multiple dichotomy counts and column percentages

Totals That Don’t Add Up

If you look at the numbers in the table, you may notice that there is a fairly largediscrepancy between the “totals” and the values that are supposedly being totaled —specifically, the totals appear to be much lower than they should be. This is becausethe count for each “category” in the table is the number of cases with a value of 1(a Yes response) for that variable, and the total number of Yes responses for all fivevariables in the multiple dichotomy set might easily exceed the total number of casesin the data file.

The total “count,” however, is the total number of cases with a Yes response for atleast one variable in the set, which can never exceed the total number of cases in thedata file. In this example, the total count of 2,081 is almost 800 lower than the totalnumber of cases in the data file. If none of these variables have missing values, thismeans that almost 800 survey respondents indicated that they don’t get news from

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any of those sources. The total count is the base for the column percentages; so thecolumn percentages in this example sum to more than the 100% displayed for thetotal column percentage.

Totals That Do Add Up

While “count” is typically a fairly unambiguous term, the above example demonstrateshow it could be confusing in the context of totals for multiple response sets, for whichresponses is often the summary statistic you really want.


E Right-click News sources in the table preview on the canvas pane and select Summary

Statistics from the pop-up context menu.

E In the Summary Statistics dialog box, select Responses in the Statistics list and clickthe arrow to add it to the Display list.

E Select Column Responses % in the Statistics list and click the arrow to add it to theDisplay list.


Figure 10-4Multiple dichotomy responses and column response percentages

For each “category” in the multiple dichotomy set, Responses is identical toCount—and this will always be the case for multiple dichotomy sets. The totals,however, are very different. The total number of responses is 3,594—over 1,500 morethan the total count and over 700 more than the total number of cases in the data file.

For percentages, the totals for Column N % and Column Responses % are both100%—but the percentages for each category in the multiple dichotomy set are muchlower for column response percentages. This is because the percentage base for

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column response percentages is the total number of responses, which in this case is3,594, resulting in much lower percentages than the column percentage base of 2,081.

Percentage Totals Greater Than 100%

Both column percentages and column response percentages yield total percentages of100% even though, in our example, the individual values in the Column N % columnclearly sum to greater than 100%. So, what if you want to show percentages based ontotal count rather than total responses but also want the “total” percentage to accuratelyreflect the sum of the individual category percentages?


E Right-click News sources in the table preview on the canvas pane and select Summary


E In the Summary Statistics dialog box, select Column Responses % (Base: Count) in theStatistics list and click the arrow to add it to the Display list.


Figure 10-5Column response percentages with count as the percentage base

Using Multiple Response Sets with Other VariablesIn general, you can use multiple response sets just like categorical variables. Forexample, you can crosstabulate a multiple response set with a categorical variable ornest a multiple response set within a categorical variable.


E Drag and drop Gender from the variable list to the left side of the Rows area on thepreview pane, nesting the multiple response set News sources within gender categories.

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Figure 10-6Table preview of nested multiple response set

E Right-click Gender in the table preview on the canvas pane and deselect (uncheck)Show Variable Label on the pop-up context menu.

E Do the same for News sources.

This will remove the columns with the variable labels from the table (since they aren’treally necessary in this case).


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Figure 10-7Multiple response set nested within a categorical variable

Statistics Source Variable and Available Summary Statistics

In the absence of a scale variable in a table, categorical variables and multiple responsesets are treated the same way regarding the statistics source variable: The innermostnested variable in the statistics source dimension is the statistics source variable. Sincethere are some summary statistics that can be assigned only to multiple response sets,this means that the multiple response set must be the innermost nested variable in thestatistics source dimension if you want any of the special multiple response summarystatistics.


E In the table preview on the canvas pane, drag and drop News sources to the left ofGender, changing the nesting order.

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Figure 10-8Categorical variable nested within multiple response set

All of the special multiple response summary statistics—responses, column responsepercentages—are removed from the table preview because the categorical variableGender is now the innermost nested variable and therefore the statistics source variable.

Luckily, the table builder “remembers” these settings. If you move News sourcesback to its previous position, nested within Gender, all of the response-relatedsummary statistics are restored to the table preview.

Multiple Category Sets and Duplicate ResponsesMultiple category sets provide one feature not available for multiple dichotomy sets:the ability to count duplicate responses. In many cases, duplicate responses in multiplecategory sets probably represent coding errors. For example, for a survey questionsuch as “What three countries do you think make the best cars?” a response of Sweden,Germany, and Sweden probably isn’t valid.

In other cases, however, duplicate responses may be perfectly valid. For example,if the question were “Where were your last three cars made?” a response of Sweden,Germany, and Sweden makes perfect sense.

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Custom Tables provides a choice for duplicate responses in multiple category sets.By default, duplicate responses are not counted, but you can request that they beincluded.


E Click Reset to clear any previous settings.

E Drag and drop Car maker, most recent cars from the variable list into the Rows areaof the canvas pane.

The icon next to the “variable” in the variable list identifies it as a multiple category set.

Figure 10-9Multiple category set icon

Figure 10-10Multiple category set in table builder preview

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For multiple category sets, the categories displayed represent the common set ofdefined value labels for all of the variables in the set (whereas for multiple dichotomysets, the “categories” are actually the variable labels for each variable in the set).

E Right-click Car maker, most recent cars in the table preview on the canvas pane andselect Categories and Totals from the pop-up context menu.

E Select (click) Total in the Categories and Totals dialog box, and then click Apply.

E Right-click Car maker, most recent cars again and select Summary Statistics fromthe pop-up context menu.

E In the Summary Statistics dialog box, select Responses in the Statistics list and clickthe arrow to add it to the Display list.


Figure 10-11Multiple category set: Counts and responses without duplicates

By default, duplicate responses are not counted; so in this table, the values for eachcategory in the Count and Responses columns are identical. Only the totals differ.


E Click the Options tab.

E Click (check) Count duplicate responses for multiple category sets.


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Figure 10-12Multiple category set with duplicate responses included

In this table, there is quite a noticeable difference between the values in the Count andResponses columns, particularly for American cars, indicating that many respondentshave owned multiple American cars.

Significance Testing with Multiple Response SetsYou can use multiple response sets in significance tests in essentially the same wayyou would use categorical variables.

For tests of independence (chi-square) or comparing column proportions (z-tests),tests are performed on counts, and Count must be one of the summary statisticsdisplayed in the table.

For multiple category sets, tests comparing column proportions or column means(t-tests) are not performed if Count duplicate responses for multiple category sets isselected on the Options tab. For more information, see “Custom Tables: OptionsTab” in Chapter 2 on p. 52.

Tests of Independence with Multiple Response Sets

This example creates a crosstabulation of a categorical variable and a multiple responseset and performs a chi-square test of independence on the crosstabulation.



E Drag and drop News sources (this is the descriptive label for the multiple dichotomyset $mltnews) from the variable list into the Columns area of the canvas pane.

E Drag and drop Gender from the variable list into the Rows area of the canvas pane.

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Figure 10-13News sources (multiple response set) and Gender selected as column and row variables

E Click the Test Statistics tab.

E Select (check) Tests of independence (chi-square).

E If it is not already selected, select Include multiple response variables in test.

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Figure 10-14Test Statistics tab

E Click OK to run the procedure.

Figure 10-15Chi-square results

The significance level of 0.068 for the chi-square test indicates that males and femalesprobably do not differ significantly in their choices of news sources (assuming youuse a significance value of 0.05 or lower as your criterion for determining statisticalsignificance).

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Comparing Column Means with Multiple Response Sets

This example calculates means of a scale variable within categories defined by amultiple response set and compares each category mean to every other category meanfor significant differences.



E Drag and drop News sources (this is the descriptive label for the multiple dichotomyset $mltnews) from the variable list into the Columns area of the canvas pane.

E Drag and drop Age of respondent into the Rows area of the canvas pane.

Figure 10-16Comparing mean age across multiple response categories

E Click the Test Statistics tab.

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E Select (check) Compare Column Means (t-tests).

E If it is not already selected, select Include multiple response variables in test.

E Click OK to run the procedure.

Figure 10-17Significance test results

Each category of the multiple response set is identified by a letter (A, B, C, D, E),and for each category for which the mean of another category is both lower anddiffers significantly from the mean of that category, the letter representing thecategory with the lower mean is displayed.

Get news from newspapers (A) has the highest mean age , and all other categorymeans differ significantly from it.

Get news from television (C) has the next highest mean age, and all remainingcategory means (B, D, and E) differ significantly from it. (C also differssignificantly from A, as previously indicated.)

The mean ages for Get news from magazine (B), Get news from radio (D), and Getnews from internet (E) do not differ significantly from each other.

Chapter

11Missing Values

Many data files contain a certain amount of missing data. A wide variety of factorscan result in missing data. For example, survey respondents may not answer everyquestion, certain variables may not be applicable to some cases, and coding errors mayresult in some values being thrown out.

There are two kinds of missing values in SPSS:

User-missing. Values defined as containing missing data. Value labels can beassigned to these values to identify why the data are missing (such as a code of 99and a value label of Not Applicable for pregnancy in males).

System-missing. If no value is present for a numeric variable, it is assigned thesystem-missing value. This is indicated by a period in the Data View of theData Editor.

SPSS offers a number of facilities that can help to compensate for the effects ofmissing data and even analyze patterns in missing data. This chapter, however, hasa much simpler goal: to describe how Custom Tables handles missing data and howmissing data affect the computation of summary statistics.

Sample Data File

The examples in this chapter use the data file missing_values.sav. This file is locatedin the Tutorial\sample_files folder within the folder in which SPSS is installed. Thisis a very simple, completely artificial data file, with only one variable and ten cases,designed to illustrate basic concepts about missing values.

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Tables without Missing Values

By default, user-missing categories are not displayed in custom tables (andsystem-missing values are never displayed).



E In the table builder, drag and drop Variable with missing values (the only variable inthe file) from the variable list into the Rows area of the canvas pane.

E Right-click the variable on the canvas pane and select Categories and Totals from thepop-up context menu.

E Click (check) Total in the Categories and Totals dialog box, and then click Apply.

E Right-click Variable with missing values in the table preview on the canvas pane againand select Summary Statistics from the pop-up context menu.



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Figure 11-1Table preview without missing values

You may notice a slight discrepancy between the categories displayed in the tablepreview on the canvas pane and the categories displayed in the Categories list (belowthe variable list on the left side of the table builder). The Categories list containsa category labeled Missing Values that isn’t included in the table preview becausemissing value categories are excluded by default. Since “values” is plural in the label,this indicates that the variable has two or more user-missing categories.


Figure 11-2Table without missing values

Everything in this table is perfectly fine. The category values add up to the totals,and the percentages accurately reflect the values you’d get using the total count asthe percentage base (for example, 3/7= 0.429, or 42.9%). The total count, however,

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is not the total number of cases in the data file; it’s the total number of cases withnon-missing values, or cases that don’t have user-missing or system-missing valuesfor that variable.

Including Missing Values in TablesE Open the table builder (Analyze menu, Tables, Custom Tables).

E Right-click Variable with missing values in the table preview on the canvas pane andselect Categories and Totals from the pop-up context menu.

E Click (check) Missing Values in the Categories and Totals dialog box, and then clickApply.

Figure 11-3Table preview with missing values category displayed

Now the table preview includes a Missing Values category. Although the table previewdisplays only one category for missing values, all user-missing categories will bedisplayed in the table.

E Right-click Variable with missing values in the table preview on the canvas pane againand select Summary Statistics from the pop-up context menu.

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E In the Summary Statistics dialog box, click (check) Custom Summary Statistics for

Totals and Subtotals.

E Select Valid N in the custom summary Statistics list and click the arrow to add itto the Display list.

E Do the same for Total N.

E Click Apply to Selection, and then click OK in the table builder to create the table.

Figure 11-4Table with missing values

The two defined user-missing categories—Don’t know and Not applicable—are nowdisplayed in the table, and the total count is now 9 instead of 7, reflecting the additionof the two cases with user-missing values (one in each user-missing category). Thecolumn percentages are also different now, because they are based on the number ofnon-missing and user-missing values. Only system-missing values are not included inthe percentage calculation.

Valid N shows the total number of non-missing cases (7), and Total N shows thetotal number of cases, including both user-missing and system-missing. The totalnumber of cases is 10, one more than the count of non-missing and user-missing valuesdisplayed as the total in the Count column. This is because there’s one case witha system-missing value.


E Right-click Variable with missing values in the table preview on the canvas pane andselect Summary Statistics from the pop-up context menu.

E Select Column Valid N % in the top Statistics list (not the custom summaries for totalsand subtotals) and click the arrow to add it to the Display list.

E Do the same for Column Total N %.

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E You can also add them both to the list of custom summary statistics for totals andsubtotals.


Figure 11-5Table with missing values and valid and total percentages

Column N % is the percentage in each category based on the number ofnon-missing and user-missing values (since user-missing values have beenexplicitly included in the table).

Column Valid N % is the percentage in each category based on only the valid,non-missing cases. These values are the same as the column percentages were inthe original table that did not include user-missing values.

Column Total N % is the percentage in each category based on all cases, includingboth user-missing and system-missing. If you add up the individual categorypercentages in this category, you’ll see that they add up to only 90%, because onecase out of the total of 10 cases (10%) has the system-missing value. Althoughthis case is included in the base for the percentage calculations, no category isprovided in the table for cases with system-missing values.

Chapter

12Formatting and CustomizingTables

Custom Tables provides the ability to control a number of table-formatting propertiesas part of the table-building process, including:

Display format and labels for summary statistics

Minimum and maximum data column width

Text or value displayed in empty cells

These settings persist within the table builder interface (until you change them, resetthe table builder settings, or open a different data file), enabling you to create multipletables with the same formatting properties without manually editing the tables aftercreating them. You can also save these formatting settings, along with all of the othertable parameters, using the Paste button in the table builder interface to paste commandsyntax into a syntax window, which you can then save as a file.

You can also change many formatting properties of tables after they have beencreated, using all of the formatting capabilities available in the Viewer for pivot tables.This chapter, however, focuses on controlling table formatting properties before thetable is created. For more information on pivot tables, use the Index tab in the Helpsystem and type pivot tables as the keyword.

Sample Data File



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Summary Statistics Display Format

Custom Tables attempts to apply relatively intelligent default formats to summarystatistics, but there will probably be times when you want to override these defaults.



E In the table builder, drag and drop Age category from the variable list into the Rowsarea on the canvas pane.

E Drag and drop Confidence in television below Age category in the Rows area, stackingthe two variables in the row dimension.

E Right-click Age category in the table preview on the canvas pane and select Select All

Row Variables from the pop-up context menu.

E Right-click Age category again and select Categories and Totals from the pop-upcontext menu.

E In the Categories and Totals dialog box, select (check) Total and then click Apply.

E Right-click either variable in the table preview on the canvas pane and select Summary


E Select Column N % in the Statistics list and click the arrow key to add it to the Displaylist.

E Select (check) Custom Summary Statistics for Totals and Subtotals.

E In the Statistics list for custom summary statistics, select Column N % and click thearrow to add it to the Display list.

E Do the same for Mean.

E Then click Apply to All.

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Figure 12-1Default display formats in table preview

The placeholder values in the table preview reflect the default format for each summarystatistic.

For counts, the default display format is nnnn—integer values with no decimalplaces.

For percentages, the default display format is nnnn.n%—numbers with a singledecimal place and a percentage sign after the value.

For the mean, the default display format is different for the two variables.

For summary statistics that aren’t some form of count (including Valid N and Total N)or percentage, the default display format is the display format defined for the variablein the Data Editor. If you look at the variables in Variable View in the Data Editor,you will see that Age category (variable agecat) is defined as having two decimalpositions, while Confidence in television (variable contv) is defined as having zerodecimal positions.

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Figure 12-2Variable View in the Data Editor

This is one of those cases where the default format probably isn’t the format you want,since it would probably be better if both mean values displayed the same number ofdecimals.

E Right-click either variable in the table preview on the canvas pane and select Summary


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For the mean, the Format cell in the Display list indicates that the format is Auto,which means that the defined display format for the variable will be used, and theDecimals cell is disabled. In order to specify the number of decimals, you first need toselect a different format.

E In the custom summary statistics Display list, click the Format cell for the mean, andselect nnnn from the drop-down list of formats.

E In the Decimals cell, enter a value of 1.

E Then click Apply to All to apply this setting to both variables.

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Figure 12-4Table preview with user-specified summary statistics display formats

Now the table preview indicates that both mean values will be displayed with onedecimal position. (You could go ahead and create this table now—but you might findthe “mean” value for Age category a little difficult to interpret, since the actual numericcodes for this variable range only from 1 to 6.)

Display Labels for Summary Statistics

In addition to the display formats for summary statistics, you can also control thedescriptive labels for each summary statistic.



E In the table builder, drag and drop Age category from the variable list into the Rowsarea on the canvas pane.

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E Drag and drop How get paid last week from the variable list into the Columns areaon the canvas pane.

E Right-click Age category in the table preview on the canvas pane and select Summary


E Select Column N % in the Statistics list and click the arrow key to add it to the Displaylist.

E Double-click anywhere in the word Column in the Label cell in the Display list to editthe contents of the cell. Delete the word Column from the label, changing the labelto simply %.

E Edit the Label cell for Count in the same way, changing the label to simply N.

While we’re here, let’s change the format of the Column N % statistic to remove theunnecessary percentage sign (since the column label indicates that the column containspercentages).

E Click the Format cell for Column N % and select nnnn.n from the drop-down list offormats.

Figure 12-5Summary Statistics dialog box with modified labels and formats

E Then click Apply to Selection.

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Figure 12-6Table preview with modified summary statistics labels

The table preview displays the modified display format and the modified labels.


Figure 12-7Table with modified summary statistics labels

Column Width

You may have noticed that the table in the above example is rather wide. One solutionto this problem would be to simply swap the row and column variables. Anothersolution is to make the columns narrower, since they seem to be much wider than

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necessary. (In fact, the reason we shortened the summary statistics labels was so thatwe could make the columns narrower.)



E In the Width for Data Columns group, select Custom.

E For the Maximum, type 36. (Make sure that the Units setting is Points.)

Figure 12-8Custom Tables dialog box, Options tab


Figure 12-9Table with reduced column widths

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Now the table is much more compact.

Display Value for Empty Cells

By default, a 0 is displayed in empty cells (cells that contain no cases). You caninstead display nothing in these cells (leave them blank) or specify a text string todisplay in empty cells.



E In the Data Cell Appearance group, for Empty Cells select Text and type None.


Figure 12-10Table with “None” displayed in empty cells

Now the four empty cells in the table display the text None instead of a value of 0.

Display Value for Missing Statistics

If a statistic cannot be computed, the default display value is a period (.), which is thesymbol used to indicate the system-missing value. This is different from an “empty”cell, and therefore the display value for missing statistics is controlled separately fromthe display value for cells that contain no cases.


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E Drag and drop Hours per day watching TV from the variable list to the top of theColumns area on the canvas, above How get paid last week.

Since Hours per day watching TV is a scale variable, it automatically becomes thestatistics source variable and the summary statistic changes to the mean.

E Right-click Hours per day watching TV in the table preview in the canvas pane andselect Summary Statistics from the pop-up context menu.

E Select Valid N in the Statistics list and click the arrow key to add it to the Display list.




E In the text field for Statistics that Cannot be Computed, type NA.

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Figure 12-12Changing the display value for statistics that cannot be computed


Figure 12-13Table with “NA” displayed for missing statistics

The text NA is displayed for the mean in three cells in the table. In each case, thecorresponding Valid N value explains why: There are no cases with which to computethe mean.

You may, however, notice what appears to be a slight discrepancy—one of thosethree Valid N values is displayed as a 0, rather than the label None that is supposed tobe displayed in cells with no cases. This is because although there are no valid cases touse to compute the mean, the category isn’t really empty. If you go back to the original

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table with just the two categorical variables, you will see that there are, in fact, threecases in this crosstabulated category. There are no valid cases, however, because allthree have missing values for the scale variable Hours per day watching TV.

Changing the Default TableLook

Many of the display properties of pivot tables can be controlled with TableLooks. Awide variety of predefined TableLooks are available, and you can control the defaultTableLook that determines the display properties applied to pivot tables when theyare created.


Options...

E Click the Pivot Tables tab.

E From the list of TableLooks, select Contrast 3.tlo.

Figure 12-14Changing the default TableLook

E Click OK.

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Figure 12-15New default TableLook applied to a newly created table

Every table you create will use this TableLook until you specify a different defaultTableLook. You can also create your own TableLooks and apply different TableLooksto tables you’ve already created. For more information, use the Index tab in the Helpsystem and type TableLooks as the keyword.

Chapter

13TABLES Command SyntaxConverter

If you have command syntax files that contain TABLES syntax that you want to convertto CTABLES syntax, a simple utility program is provided to help you get started withthe conversion process. There are, however, significant differences between TABLES

and CTABLES functionality, and it is likely that you will find that the utility programcannot convert some of your TABLES syntax jobs or may generate CTABLES syntaxthat produces tables that do not closely resemble the original tables produced by theTABLES command. In most cases, you can edit the converted syntax to produce a tableclosely resembling the original.

The utility program is designed to:

Create a new syntax file from an existing syntax file. The original syntax fileis not altered.

Convert only TABLES commands in the syntax file. Other commands in the fileare not altered.

Retain the original TABLES syntax in commented form.

Identify the beginning and end of each conversion block with comments.

Identify TABLES syntax commands that could not be converted.

Convert command syntax files that follow either interactive or production modesyntax rules.

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The utility program may convert TABLES commands incorrectly under somecircumstances, including TABLES commands that contain:

Parenthesized variable names with the initial letters “sta” or “lab” in the TABLESsubcommand if the variable is parenthesized by itself—for example, var1 by(statvar) by (labvar). These will be interpreted as the (STATISTICS)and (LABELS) keywords.

SORT subcommands that use the abbreviations A or D to indicate ascending ordescending sort order. These will be interpreted as variable names.

The utility program cannot convert TABLES commands that contain:

Syntax errors.

OBSERVATION subcommands that refer to a range of variables using the TOkeyword (for example, var01 TO var05).

String literals broken into segments separated by plus signs (for example, TITLE"My" + "Title").

Macro calls that, in the absence of macro expansion, would be invalid TABLESsyntax. Since the converter does not expand the macro calls, it treats them as ifthey were simply part of the standard TABLES syntax.

The utility program will not convert TABLES commands contained in macros. Allmacros are unaffected by the conversion process.

Using the Conversion Utility Program

The conversion utility program, SyntaxConverter.exe, is installed in the same directoryas SPSS. It is designed to run from a command prompt. The general form of thecommand is:

[SPSS install location]\syntaxconverter.exe [path]\inputfilename.sps [path]\outputfilename.sps

If any directory names contain spaces, enclose the entire path and filename in quotationmarks, as in:

"c:\program files\spss\syntaxconverter.exe" c:\myfiles\oldfile.sps "c:\new files\newfile.sps"

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TABLES Command Syntax Converter

Interactive versus Production Mode Command Syntax Rules

The conversion utility program can convert command files that use interactive orproduction mode syntax rules.

Interactive. The interactive syntax rules are:

Each command begins on a new line.

Each command ends with a period (.).

Production mode. The SPSS Production Facility and commands in files accessed via theINCLUDE command in a different command file use production mode syntax rules:

Each command must begin in the first column of a new line.

Continuation lines must be indented at least one space.

The period at the end of the command is optional.

If your command files use production mode syntax rules and don’t contain periodsat the end of each command, you need to include the command line switch -b (or /b)when you run SyntaxConverter.exe, as in:

"c:\program files\spss\syntaxconverter.exe" -b c:\myfiles\oldfile.sps c:\myfiles\newfile.sps

You can also run the syntax converter with the script SyntaxConverter.sbs, located inthe Scripts directory of the SPSS installation directory.

E From the menus choose:Utilities

Run Script...

E Navigate to the Scripts directory and select SyntaxConverter.sbs.

This will open a simple dialog box where you can specify the names and locations ofthe old and new command syntax files.

Index

captions

custom tables, 54chi-square statistic

custom tables, 155collapsing categories

custom tables, 106column means statistics

custom tables, 161column proportions statistics

custom tables, 167column width

controlling in custom tables, 52, 204comperimeter tables, 51, 111controlling number of decimals displayed, 68corner labels

custom tables, 54count

vs. valid N, 144crosstabulation

custom tables, 66CTABLES

converting TABLES command syntax to

CTABLES, 211custom tables

captions, 54categorical variables, 29changing labels for summary statistics, 62changing measurement level, 29changing summary statistics dimension, 43collapsing categories, 106column width, 52compact view, 85

comperimeter tables, 51, 111controlling number of decimals displayed, 37converting TABLES command syntax to

CTABLES, 211corner labels, 54crosstabulation, 66custom totals, 43display formats, 37empty cells, 52excluding categories, 46, 70hiding statistics labels, 60hiding subtotaled categories, 106how to build a table, 33layer variables, 88, 91, 93marginal totals, 69mean-frequency tables, 43missing values exclusion for scale summaries, 52multiple category sets, 52multiple response sets, 29, 175nesting layer variables, 93nesting variables, 80, 85percentages, 39, 41, 62, 67percentages for multiple response sets, 41printing layered tables, 94reordering categories, 46row vs. column percentages, 62scale variables, 29showing and hiding variable names and labels, 37significance testing and multiple response, 174simple tables for categorical variables, 60sorting categories, 70stacking variables, 77, 79

215

216

Index

statistics source dimension, 67subtotals, 46, 95summary statistics, 39, 41–42summary statistics display formats, 45swapping row and column variables, 87table of frequencies, 51, 111tables of variables with shared categories, 51, 111test statistics, 55, 155titles, 54totals, 46, 64, 95totals in tables with excluded categories, 70value labels for categorical variables, 29

custom total summary statistics, 133

date

including current date in custom tables, 54decimal places

controlling number of decimals displayed in

custom tables, 37, 68, 198deleting categories

custom tables, 46, 70different summary statistics for different variables

stacked tables, 146display formats, 68

summary statistics in custom tables, 45, 198displaying category values, 138

empty cells

displayed value in custom tables, 52, 206excluding categories

custom tables, 46, 70

group totals, 98grouped summaries

scale variables, 148

hiding statistics labels in custom tables , 60

labels

changing label text for summary statistics, 202layer variables

custom tables, 88, 91, 93nesting layer variables, 93printing layered tables, 94stacking layer variables, 91

maximum

custom tables, 42mean, 141

custom tables, 42mean-frequency tables, 43, 133measurement level

changing in custom tables, 29median, 143

custom tables, 42minimum

custom tables, 42missing values, 144, 191

effect on percentage calculations, 194including in custom tables, 194

mode

custom tables, 42multiple response sets, 175

duplicate responses in multiple category sets, 52percentages, 41significance testing, 174, 185, 188

nesting variables

custom tables, 80, 85scale variables, 152

217

Index

omitting categories

custom tables, 70

percentages

in custom tables, 39, 41, 62, 67missing values, 194multiple response sets, 41

printing tables with layers , 94

range

custom tables, 42reordering categories

custom tables, 46

scale variables

grouped summaries, 148multiple summary statistics, 143nesting, 152stacking, 141summaries grouped by row and column

categorical variables, 149summary statistics, 141

significance tests

custom tables, 55multiple response sets, 185, 188

sorting categories

custom tables, 70stacking variables

custom tables, 77, 79different summary statistics for different

variables, 146multiple summary statistics source variables, 130scale variables, 141stacking layer variables, 91

standard deviation

custom tables, 42

statistics

custom total summary statistics, 133stacked tables, 130summary statistics, 123

subgroup totals, 98subtotals, 103

custom tables, 46, 95hiding subtotaled categories, 106

sum

custom tables, 42summary statistics, 123

changing label text, 202custom total summary statistics, 133different summaries for different variables in

stacked tables, 146display format, 198source dimension, 124source variable, 124stacked tables, 130

summary statistics source variable

scale variables, 152system-missing values, 191

table of frequencies

custom tables, 51, 111TableLooks

changing the default TableLook, 209tables

custom tables, 29TABLES

converting TABLES command syntax to

CTABLES, 211test statistics

custom tables, 55, 155time

including current time in custom tables, 54

218

Index

titles

custom tables, 54total N, 194totals

custom tables, 46, 64, 95display position, 98excluded categories, 97group totals, 98layers, 101marginal totals for custom tables, 69nested tables, 98

user-missing values, 191

valid N, 144, 194custom tables, 42

values

displaying category labels and values, 138values and value labels, 138variable labels

suppressing display in custom tables, 37variance

custom tables, 42

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